TY - GEN A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Maintenance scheduling optimisation of Reverse Osmosis Networks (RONs) via a multistage Optimal Control reformulation T2 - Desalination N2 - State-of-the-art approaches for membrane cleaning scheduling have focused on the Mixed-Integer Nonlinear Programming (MINLP) formulation so far, a strategy leading to a combinatorial problem that does not capture accurately the dynamic behaviour of the system. In this work, the Reverse Osmosis (RO) cleaning scheduling problem is solved using a novel approach based on the Multistage Integer Nonlinear Optimal Control Problem (MSINOCP) formulation. The approach produces an automated solution for the membrane cleaning scheduling, which also obviates the need for any form of combinatorial optimisation. Two different simulations, for 26 and 52 periods of operation (each period with a duration of one week), are carried out to illustrate the application of the proposed framework and the total cost is 1.17 and 2.48 10⁷ €, respectively. The RO network configuration considers 2 stages, each with 3 individual RO modules. The results show evidently that the new proposed solution framework can solve successfully this type of problems, even for large scale configurations, long time horizons and arbitrary realistic complexity of the underlying dynamic model of the RO process considered. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S0011916422005604?via%3Dihub U6 - https://doi.org/10.1016/j.desal.2022.116105 SN - 1873-4464 SN - 0011-9164 VL - 543 ER - TY - GEN A1 - Campos, Jean C. A1 - Manrique, Jose A1 - Dorneanu, Bogdan A1 - Ipanaqué, William A1 - Arellano-Garcia, Harvey T1 - A smart decision framework for the prediction of thrips incidence in organic banana crops T2 - Ecological Modelling N2 - Various pests which diminish the quality of the fruit have a big influence on the organic banana production in the Piura region of Peru (and not only) and prevent it from being sold on the international market. In this study, a framework for facilitating the prediction of the pest incidence in organic banana crops is developed. To achieve this, a data acquisition system with smart sensors is implemented to monitor the meteorological variables that influence the growth of the pests. The proposed framework is utilised for the assessment of various mathematical representations of the pest incidence. These models are adapted from population growth functions and built in such way as to predict the behaviour of the insects at non-regular time intervals. A hybrid approach, combining mechanistic and data-based methods is utilised for the development of the models. Both linear and nonlinear dynamic relationships with the temperature are assumed. The results show that nonlinear model representations have greater accuracy (a fit index of more than 70%), which provides a basis for improving pest management actions on the organic banana farms. KW - Pest control KW - Organic banana KW - Mathematical modelling KW - Smart sensors KW - Precision agriculture Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S0304380022002484?via%3Dihub U6 - https://doi.org/10.1016/j.ecolmodel.2022.110147 SN - 1872-7026 VL - 473 ER - TY - GEN A1 - Yentumi, Richard A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Mathematical modelling, simulation and optimisation of an indirect water bath heater at the Takoradi distribution station (TDS) T2 - Computer Aided Chemical Engineering N2 - In this contribution, a dynamic first principles model of an existing 3.01 MW natural gas fired water bath heater (WBH) in operation at the Takoradi Distribution Station (TDS) in Ghana is developed primarily to predict the outlet temperature of the natural gas stream being heated. The model is intended to be applied during operations to provide useful data to optimise material and energy consumption, as well as minimise CO2 emissions. Due to the low thermal efficiencies of WBHs, even small improvements in efficiency can result in significant savings. The firetube and process coils are both modelled as onedimensional (1D) thin-walled tubes and the entire model incorporates mass and energy conservation equations, heat transfer rate relations and rigorous thermodynamic p-V-T relations. In contrast to what commonly exists in literature, this model accurately estimates the enthalpy change of the natural gas stream being heated by accounting for its enthalpy departure correction term due to pressure, in addition to the ideal gas heat capacity relation which is a function of only temperature. The coupled ordinary differential and algebraic equations are implemented using gPROMS® ModelBuilder® V4.2.0, a commercial modelling and simulation software. Verification of the model results showed good agreement between the model predictions and actual on field measurements. With excess air at 15%, the simulation results closely approximate measured data with an absolute error of about 0.31 %. More importantly, the results show that significant savings of up to 30% per annum can be made through optimal operation of the water bath heater. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323851596501639?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-85159-6.50163-9 SN - 1570-7946 VL - 49 SP - 979 EP - 984 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Vassiliadis, Vassilios S. A1 - Arellano-Garcia, Harvey T1 - Maintenance scheduling optimization for decaying performance nonlinear dynamic processes T2 - Computer Aided Chemical Engineering N2 - A first contribution of this paper is an overview of the research efforts and contributions over several decades in the area of scheduling maintenance optimization for decaying performance dynamic processes. Following breakthrough ideas and implementation in the area of heat exchanger networks for optimal scheduling of cleaning actions subject to exchanger surface fouling, these concepts were transferred successfully to the area of scheduling catalyst replacement actions in catalytic reactor networks. This necessary overview leads to the main, second contribution aimed with this work: its application to restorative maintenance scheduling in the area of RON regeneration actions planning, as well as point to new areas where this approach can be fruitfully applied to and extended into in the near future – particularly enhancing model descriptions that include general types of planning uncertainty. The effectiveness and efficacy of the approach is demonstrated computationally in this work. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323851596500841?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-85159-6.50084-1 SN - 1570-7946 VL - 49 SP - 505 EP - 510 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Masham, Elliot A1 - Mechleri, Evgenia A1 - Arellano-Garcia, Harvey T1 - Centralised versus localised supply chain management using a flow configuration model T2 - Computer Aided Chemical Engineering N2 - Traditional food supply chains are often centralised and global in nature. Moreover they require a large amount of resource which is an issue in a time with increasing need for more sustainable food supply chains. A solution is to use localised food supply chains, an option theorised to be more sustainable, yet not proven. Therefore, this paper compares the two systems to investigate which one is more environmentally friendly, cost efficient and resilient to disruption risks. This comparison between the two types of supply chains, is performed using MILP models for an ice cream supply chain for the whole of England over the period of a year. The results obtained from the models show that the localised model performs best environmentally and economically, whilst the traditional, centralised supply chain performs best for resilience. Y1 - 2019 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780128186343502319?via%3Dihub U6 - https://doi.org/10.1016/B978-0-12-818634-3.50231-9 SN - 1570-7946 VL - 46 SP - 1381 EP - 1386 ER - TY - GEN A1 - Yentumi, Richard A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Modelling and optimal operation of a natural gas fired natural draft heater T2 - Computer Aided Chemical Engineering N2 - Current industrial trends promote reduction of material and energy consumption of fossil fuel burning, and energy-intensive process equipment. It is estimated that approximately 75% of the energy consumption in hydrocarbon processing facilities is used by such equipment as fired heater, hence even small improvements in the energy conservation may lead to significant savings [1, 2]. In this work, a mathematical modelling and optimisation study is undertaken using gPROMS® ProcessBuilder® to determine the optimal operating conditions of an existing API 560 Type-E vertical-cylindrical type natural draft fired heater, in operation at the Atuabo Gas Processing Plant (GPP), in the Western Region of Ghana. It is demonstrated that the optimisation results in significant reduction of fuel gas consumption and operational costs. Y1 - 2019 UR - https://www.sciencedirect.com/science/article/abs/pii/B978012818634350165X?via%3Dihub U6 - https://doi.org/10.1016/B978-0-12-818634-3.50165-X SN - 1570-7946 VL - 46 SP - 985 EP - 990 ER - TY - GEN A1 - Mechleri, Evgenia A1 - Sidnell, Tim A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Optimisation and control of a distributed energy resource network using Internet-of-Things technologies T2 - Computer Aided Chemical Engineering N2 - This work investigates the use of variable pricing to control electricity imported and exported to and from both fixed and unfixed distributed energy resource network designs within the UK residential sector. It was proven that networks which utilise much of their own energy and import little from the national grid are barely affected by variable import pricing, but are encouraged to export more energy to the grid by dynamic export pricing. Dynamic import and export pricing increased CO2 emissions due to feed-in tariffs which encourages CHP generation over lower-carbon technologies such as solar panels or wind turbines. Y1 - 2019 UR - https://www.sciencedirect.com/science/article/abs/pii/B978012818634350014X?via%3Dihub U6 - https://doi.org/10.1016/B978-0-12-818634-3.50014-X SN - 1570-7946 VL - 46 SP - 79 EP - 84 ER - TY - GEN A1 - Arellano-Garcia, Harvey A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia T1 - Devicification of Food Process Engineering T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering N2 - Food and beverages industry is facing major challenges in the years to come, as the world population is expected to grow, accompanied by a growing need for energy, feed and fuel. Much of the processing in the food industry is performed in small-scale decentralized plants, with relatively few possibilities of energy recovery. Innovation in areas such as process and product modeling, process intensification and process control enable the development of new manufacturing pathways, more efficient, versatile, selective and sustainable. Furthermore, the addition of Internet of Things elements and the way they connect to the physical process will ensure continuous communication between the various actors of the food value chain. This will enable the design of new compact, scalable, flexible, modular, and automated production equipment that will offer food manufacturers the ability to respond rapidly, economically, accurately and flexibly to the consumer demands. Y1 - 2019 UR - https://www.sciencedirect.com/science/article/pii/B9780124095472144269?via%3Dihub U6 - https://doi.org/10.1016/B978-0-12-409547-2.14426-9 VL - 2019 ER - TY - GEN A1 - De Mel, Ishanki A1 - Demis, Panagiotis A1 - Dorneanu, Bogdan A1 - Klymenko, Oleksiy A1 - Mechleri, Evgenia A1 - Arellano-Garcia, Harvey T1 - Global Sensitivity Analysis for Design and Operation of Distributed Energy Systems T2 - Computer Aided Chemical Engineering N2 - Distributed Energy Systems (DES) are set to play a vital role in achieving emission targets and meeting higher global energy demand by 2050. However, implementing these systems has been challenging, particularly due to uncertainties in local energy demand and renewable energy generation, which imply uncertain operational costs. In this work we are implementing a Mixed-Integer Linear Programming (MILP) model for the operation of a DES, and analysing impacts of uncertainties in electricity demand, heating demand and solar irradiance on the main model output, the total daily operational cost, using Global Sensitivity Analysis (GSA). Representative data from a case study involving nine residential areas at the University of Surrey are used to test the model for the winter season. Distribution models for uncertain variables, obtained through statistical analysis of raw data, are presented. Design results show reduced costs and emissions, whilst GSA results show that heating demand has the largest influence on the variance of total daily operational cost. Challenges and design limitations are also discussed. Overall, the methodology can be easily applied to improve DES design and operation. Y1 - 2020 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780128233771502548?via%3Dihub U6 - https://doi.org/10.1016/B978-0-12-823377-1.50254-8 SN - 1570-7946 VL - 48 SP - 1519 EP - 1524 ER - TY - GEN A1 - Menzhausen, Robert A1 - Merino, Manuel A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - A Fuzzy Control Approach for an Industrial Refrigeration System T2 - Computer Aided Chemical Engineering N2 - This contribution presents the development of a model for the refrigeration plant used for mangos, which is able to simulate both the chamber and the fruit temperatures. The model is developed from energy balances for each section of the refrigeration system and the fruits, and is the basis for the setup of a fuzzy controller, capable of regulating continuously the compressor’s frequency to achieve the desired temperature. The model is able to accurately predict the chamber temperature profiles, but is more sensitive when simulating the fruit temperature. The fuzzy controller is able to achieve the set-point more accurately and in a shorter time than the on/off control, achieving a decrease in energy consumption as well. Y1 - 2020 UR - https://www.sciencedirect.com/science/article/abs/pii/B978012823377150210X?via%3Dihub U6 - https://doi.org/10.1016/B978-0-12-823377-1.50210-X SN - 1570-7946 VL - 48 SP - 1255 EP - 1260 ER - TY - GEN A1 - Clarke, Fiona A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia A1 - Arellano-Garcia, Harvey T1 - Optimal design of heating and cooling pipeline networks for residential distributed energy resource systems T2 - Energy N2 - This paper presents a mixed integer linear programming model for the optimal design of a distributed energy resource (DER) system that meets electricity, heating, cooling and domestic hot water demands of a neighbourhood. The objective is the optimal selection of the system components among different technologies, as well as the optimal design of the heat pipeline network to allow heat exchange between different nodes in the neighbourhood. More specifically, this work focuses on the design, interaction and operation of the pipeline network, assuming the operation and maintenance costs. Furthermore, thermal and cold storage, transfer of thermal energy, and pipelines for transfer of cold and hot water to meet domestic hot water demands are additions to previously published models. The application of the final model is investigated for a case-study of a neighbourhood of five houses located in the UK. The scalability of the model is tested by also applying the model to a neighbourhood of ten and twenty houses, respectively. Enabling exchange of thermal power between the neighbours, including for storage purposes, and using separate hot and cold pipeline networks reduces the cost and the environmental impact of the resulting DER. Y1 - 2021 UR - https://www.sciencedirect.com/science/article/pii/S0360544221016789?via%3Dihub U6 - https://doi.org/10.1016/j.energy.2021.121430 SN - 1873-6785 SN - 0360-5442 VL - 235 ER - TY - GEN A1 - Sidnell, Tim A1 - Clarke, Fiona A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia A1 - Arellano-Garcia, Harvey T1 - Optimal design and operation of distributed energy resources systems for residential neighbourhoods T2 - Smart Energy N2 - Different designs of distributed energy resources (DER) systems could lead to different performance in reducing cost, environmental impact or use of primary energy in residential networks. Hence, optimal design and management are important tasks to promote diffusion against the centralised grid. However, current operational models for such systems do not adequately analyse their complexity. This paper presents the results of a mixed-integer linear programming (MILP) model of distributed energy systems in the residential sector which builds up on previous work in this field. A superstructure optimisation model for design and operation of DER systems is obtained, providing a more holistic overview of such systems by including the following novel elements: a) Design and utilisation of a network with integrated heating/cooling pipelines and microgrid connections between neighbourhoods; b) Exploration of use of feed-in tariffs (FITs), renewable heat incentives (RHIs) and the ability to buy/sell from/to the national grid. It is shown that the (DER network mitigates around 30–40% of the CO2 emissions per household, compared with “traditional generation”. Money from FITs, RHIs and sales to the grid, as well as reduced grid purchases, make DER networks far more economical, and even profitable, compared to the traditional energy consumption. Y1 - 2021 UR - https://www.sciencedirect.com/science/article/pii/S2666955221000496?via%3Dihub U6 - https://doi.org/10.1016/j.segy.2021.100049 SN - 2666-9552 VL - 4 ER - TY - CHAP A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Heshmat, Mohamed A1 - Gao, Yang T1 - A framework for intelligent monitoring and control of chemical processes with multi-agent systems T2 - Industry 4.0 – Shaping The Future of The Digital World N2 - Industry 4.0 is transforming chemical processes into complex, smart cyber-physical systems that require intelligent methods to support the operators in taking decisions for better and safer operation. In this paper, a multi-agent cooperative-based model predictive system for monitoring and control of a chemical process is proposed. This system uses ontology to formally represent the system knowledge. By integrating the cooperative-based model predictive controller with the multi-agent system, the control can be improved, and the process can be converted into a self-adaptive system. Y1 - 2020 UR - https://www.taylorfrancis.com/chapters/edit/10.1201/9780367823085-04/framework-intelligent-monitoring-control-chemical-processes-multi-agent-systems-dorneanu-arellano-garcia-heshmat-gao SN - 9780367823085 U6 - https://doi.org/10.1201/9780367823085-04 SP - 18 EP - 23 PB - CRC Press CY - London, United Kingdom ET - 1st edition ER - TY - GEN A1 - Yentumi, Richard A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Optimal Operation of an Industrial Natural Gas Fired Natural Draft Heater T2 - Chemical Engineering Journal Advances N2 - In this work, a custom dynamic mathematical model of an industrial vertical-cylindrical type natural gas fired natural draft heater is developed using gPROMS® ProcessBuilder®. The integrated model comprises sub-models for each of the distinct sections of the fired heater which are connected by mass and energy flows. The temperature profiles of the tubular coils and the process fluid, a heat transfer fluid (HTF) are modelled using the distributed parameter system (DPS) in the axial direction (1D). The flue gas temperature in each section is modelled using the lumped parameter approach. Published empirical methods and correlations are used for estimating some unknown model parameters. The resulting model is a system of partial differential-algebraic equations (PDAEs) and serves as a basis for conducting an optimisation study to aid decision-making and to identify the best operating conditions within the specified constraints that minimise the daily operational costs. Through process simulation studies, the model predictions are adjusted to closely approximate collected actual plant data. It is demonstrated through the optimisation study that significant reduction in fuel gas consumption can be achieved compared to the current operating consumption levels. The developed models can be extended for use by other hydrocarbon processing plant operators with slight modifications, by specifying geometric parameters, HTF thermophysical properties, fuel gas composition and properties, among others. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S2666821122001144?via%3Dihub U6 - https://doi.org/10.1016/j.ceja.2022.100354 SN - 2666-8211 VL - 11 ER - TY - GEN A1 - De Mel, Ishanki A1 - Demis, Panagiotis A1 - Dorneanu, Bogdan A1 - Klymenko, Oleksiy A1 - Mechleri, Evgenia A1 - Arellano-Garcia, Harvey T1 - Global sensitivity analysis for design and operation of distributed energy systems: A two-stage approach T2 - Sustainable Energy Technologies and Assessments N2 - Distributed Energy Systems (DES) can play a vital role as the energy sector faces unprecedented changes to reduce carbon emissions by increasing renewable and low-carbon energy generation. However, current operational DES models do not adequately reflect the influence of uncertain inputs on operational outputs, resulting in poor planning and performance. This paper details a methodology to analyse the effects of uncertain model inputs on the primary output, the total daily cost, of an operational model of a DES. Global Sensitivity Analysis (GSA) is used to quantify these effects, both individually and through interactions, on the variability of the output. A Mixed-Integer Linear Programming model for the DES design is presented, followed by the operational model, which incorporates Rolling Horizon Model Predictive Control. A subset of model inputs, which include electricity and heating demand, and solar irradiance, is treated as uncertain using data from a case study. Results show reductions of minimum 25% in the total annualised cost compared to a traditional design that purchases electricity from the centralised grid and meets heating demand using boilers. In terms of carbon emissions, the savings are much smaller, although the dependency on the national grid is drastically reduced. Limitations and suggestions for improving the overall DES design and operation are also discussed in detail, highlighting the importance of incorporating GSA into the DES framework. Y1 - 2023 UR - https://www.sciencedirect.com/science/article/pii/S2213138823000565?dgcid=author U6 - https://doi.org/10.1016/j.seta.2023.103064 SN - 2213-1388 VL - 56 ER - TY - GEN A1 - Jurischka, Constantin A1 - Dorneanu, Bogdan A1 - Stollberg, Christian A1 - Arellano-Garcia, Harvey T1 - A novel approach to continuous extraction of active ingredients from essential oils through combined chromatography T2 - Computer Aided Chemical Engineering N2 - This contribution introduces a combined liquid chromatography purification designed for a continuous and resource-efficient process, integrating the rotating columns and the simulated bed principles. The approach is demonstrated and validated based on bisabolol oxides A and B, which are effective ingredients with anti-inflammatory and spasmolytic effects, prepared from chamomile essential oil. The results show superior efficiency to the traditional selective methods for isolating ingredients from multicomponent mixtures, as well as reduction in resources and costs. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323958790501296?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-95879-0.50129-6 SN - 1570-7946 VL - 51 SP - 769 EP - 774 ER - TY - GEN A1 - Estrada, Carlos A. A1 - Manrique-Silupú, José A1 - Ipanaqué, William A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - A model-based approach for the prediction of banana rust thrips incidence from atmospheric variables T2 - Computer Aided Chemical Engineering N2 - This work focuses on the development of a mathematical model for the population growth of banana red rust thrips (Chaetanaphothrips signipennis) based on a modified temperature-based growth rate with the addition of climatic variables, such as relative humidity, wind speed and rainfall rate. The aim is to enable better prediction of the pest incidence and improve decision making, productivity, as well as quantifying the influence of these variables on the development of red rust thrips. The developed model is then compared with current solutions for predicting the pest incidence, showing improved accuracy (higher than 67%) versus experimental data, for which the state-of-the-art models indicate extremely poor fits. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323958790500850?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-95879-0.50085-0 SN - 1570-7946 VL - 51 SP - 505 EP - 510 ER - TY - GEN A1 - Amao, Khalid A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Economic Analysis of Novel Pathways for Recovery of Lithium Battery Waste T2 - Computer Aided Chemical Engineering N2 - Using the Umicore process, a current state-of-the-art recycling in the metal recovery industry for lithium battery waste, as a baseline, this contribution examines economic and environmentally friendly solutions for effective metal recovery from spent LIBs. At the same time, possible synergies between existing resource use from other manufacturing and waste treatment industries are considered as valuable input to metal recycling, while also reducing the amount of atmospheric carbon. This further presents a case for possible integration of various waste management approaches as a single business unit for economic incentive and profitability for possible investment. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B978032395879050271X?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-95879-0.50271-X SN - 1570-7946 VL - 51 SP - 1621 EP - 1626 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Schnitzlein, Klaus A1 - Arellano-Garcia, Harvey T1 - A novel approach to modelling trickle bed reactors T2 - Computer Aided Chemical Engineering N2 - In this contribution, the development of a toolbox for the simulation of trickle bed reactors based on a model able to account for the local properties of the liquid and gas flow in a packed bed at particle scale is introduced. The implementation uses a modular and flexible setup, with local liquid distribution considered as a function of the operating conditions and the physical properties of the three phases. Moreover, the impact of the local incomplete wetting on the conversion, as well as the mass transport and kinetics at both particle and reactor scale are accounted for. Furthermore, different particle geometries are considered, and the model is able to reliably predict the performance of the catalytic trickle bed reactors. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323958790500473?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-95879-0.50047-3 SN - 1570-7946 VL - 51 SP - 277 EP - 282 ER - TY - CHAP A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Ruan, Hang A1 - Mohamed, Abdelrahim A1 - Xiao, Pei A1 - Heshmat, Mohamed A1 - Gao, Yang T1 - Towards fault detection and self-healing of chemical processes over wireless sensor networks T2 - Industry 4.0 – Shaping The Future of The Digital World N2 - This contribution introduces a framework for the fault detection and healing of chemical processes over wireless sensor networks. The approach considers the development of a hybrid system which consists of a fault detection method based on machine learning, a wireless communication model and an ontology-based multi-agent system with a cooperative control for the process monitoring. Y1 - 2020 UR - https://www.taylorfrancis.com/chapters/edit/10.1201/9780367823085-02/towards-fault-detection-self-healing-chemical-processes-wireless-sensor-networks-dorneanu-arellano-garcia-ruan-mohamed-xiao-heshmat-gao SN - 9780367823085 U6 - https://doi.org/10.1201/9780367823085-02 SP - 9 EP - 14 PB - CRC Press CY - London, United Kingdom ET - 1st edition ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Heshmat, Mohamed A1 - Gao, Yang T1 - Ontology Based Decision Making for Process Control T2 - 2019 AIChE Annual Meeting N2 - In the context of Industry 4.0, engineering systems and manufacturing processes are becoming increasingly complex, combining the physical world of the processing units with the cyber world of the wireless sensing and communication networks, big data analytics, ubiquitous computing and other elements that the Industrial Internet of Things technologies. The fields of ontology, knowledge management and decision-making systems have matured significantly in the recent years and their integration with the cyber-physical system (CPS) facilitates and improves the effectiveness of decision-support systems (DSS) [1]. Yet, this comes with an increase in the system’s complexity and a need for deployment of intelligent systems for process systems engineering (PSE) applications, that should adapt to the continuously new requirements of Industry 4.0. Considering the large number of devices existent in a CPS, distributed methods are required to transfer the computational load from centralised to local (decentralised) controllers. This has led to the motivation of applying multi-agent systems (MASs) methodologies as a solution to distributed control as a computational paradigm [2]. An agent can be defined as an entity placed in an environment that can sense different parameters used to make a decision based on the goals of the entity. A MAS is a computerised system composed of multiple interacting agents exploited to solve a problem. Their salient features, which include efficiency, low cost, flexibility, and reliability, make it an effective solution for solving tasks [3]. Usually, DSS adopt a rule-based or logic-based representation scheme [4]. For this reason, ontologies have attracted the attention of the PSE community as a convenient means for knowledge representation [1, 5]. An ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts, and it serves as a library of knowledge to efficiently build intelligent systems and as a shared vocabulary for communication between interacting human and/or software agents [6]. In this paper, a multi-agent cooperative-based model predictive control (MPC) system for monitoring and control of a chemical process is proposed. The system uses ontology to formally represent the system knowledge at process, communication and decision-making level. The application of the proposed framework is discussed for a chemical process that produces iso-octane. A cooperative MPC is implemented to achieve the control of the plant. This protocol is defined using a simple algorithm to reach an agreement regarding the state of a number of N agents [7]. The monitoring feature is defined by means of a MAS, consisting of follower agents (FAs), a coordinator agent (CA) and a monitor agent (MoA), that is integrated with the MPC. The MAS has two main tasks: a) decide optimal connectivity between the distributed MPCs for safer and better operation; and b) monitor the system and detect any deviation in the behaviour. The addition of the MAS makes the cooperative MPC controller more efficient by taking advantage of the communication between the various elements of the CPS. Using the knowledge form the ontology and the agents’ sharing capabilities, the system can detect faster any deviation compared to standard operation. The framework can easily be adapted for other control approaches by very simple modifications in the structure and objectives. A practical demonstration in a pilot plant environment is envisaged for the future. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/145e-ontology-based-decision-making-process-control SN - 978-0-8169-1112-7 ER - TY - GEN A1 - Yusuf, Ifrah A1 - Dorneanu, Bogdan A1 - Avignone-Rossa, Claudio A1 - Arellano-Garcia, Harvey T1 - Synthesis and Characterization of Hydrochars Produced By Hydrothermal Carbonization of Banana Peels T2 - 2019 AIChE Annual Meeting N2 - Hydrothermal carbonisation (HTC) is a thermochemical process which imitates the natural coalification of biomass. If the natural process requires some hundred to some million years, depending on the type of coal produced, HTC needs less than half a day for the transformation of biomass into materials quite similar to brown coal [1].The organic feedstock is reacted with water at mild temperature (130-300 0C) compared to other thermochemical processes such as pyrolysis, gasification, or flash carbonisation, and under autogenous pressures (10-96 bar). The result is a homogeneous carbon-rich solid, a high-strength process liquid, and a gaseous product mainly consisting of CO2 [2]. Compared to the biomass feedstock, biochar possesses a higher heating value and higher carbon content, has a lower ash content, more surface oxygen-containing groups, and it can lead to lower emissions of greenhouse gases [3]. The difference in chemical composition of the final products depend on the reaction mechanisms that occur during HTC, which include hydrolysis, dehydration, decarboxylation, aromatization and re-condensation. Although these processes generally occur in this order, they do not operate in a successive manner; instead they occur simultaneously during HTC and are interconnected with each other [4]. The main purpose of this work is to evaluate the technological feasibility of converting banana peel residues in useful products using the HTC, towards a localised production strategy to harness the value of the waste for improving the livelihoods of rural agricultural communities. A study of the prevailing reactions, their rates and products from banana peel processing through HTC is used to support the optimisation of the reactor design. The products’ yield is influenced by factors such as temperature, feed solid content, the nature of the biomass, and residence time. A detailed characterisation of all the products obtained from HTC is conducted. Considerable effort is needed to comprehend their stability and quality and thereby the ongoing process reactions and upgrading needs. Characterisation methods, such as GC/MS NMR, and HPLC for product analysis are critical to understand the nature of the reactive species influencing product quality and yield. Additionally, as efficient separation from an aqueous phase increases the yield of useful products, the separation of the main products and water is investigated. Furthermore, the feasibility of the recycle and re-use of the process water is analysed. The improvement and reuse of the hydrochar are appealing for applications such as solid fuel, pre-cursor for activated carbon, adsorbent, soil amendment or carbon sequestering biochar. Moreover, the use of HTC to convert banana peels into products such as hydrochar or bio-oil will enable the local rural communities create value from something they are discarding as waste. The hydrochar, processed into pellet form to increase its bulk density in order to reduce storage and transportation costs, can be directly used as a solid fuel that can be burned for energy. This is particularly effective for small and medium farms dispersed over extended areas, due to the significant reduction of expenses and environmental impact. The hydrochar can be added to soil to enhance the effects of the fertilisers, by reducing the amount of fertiliser lost through surface run-off. In addition, it increases the amount of water that can be retained by sandy soils, with a low available water capacity. The use of banana peels will produce highly effective sorbent hydrochars to be used for heavy metals removal from water. Moreover, our results suggest that the liquid fraction obtained from the hydrothermal processing of banana peel is a good feedstock for Microbial Fuel Cells. The hydrochar obtained in the process has shown to present several properties, such as the removal of various types of pollutants from contaminated waters. Therefore, the integration of HTC to convert banana peels into hydrochar and the utilization of the liquid by-product as feedstock for bioelectrochemical system (BES) technology enables full utilization of an otherwise recalcitrant waste. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/397d-synthesis-and-characterization-hydrochars-produced-hydrothermal-carbonization-banana-peels SN - 978-0-8169-1112-7 ER - TY - GEN A1 - De Mel, Ishanki A1 - Mechleri, Evgenia A1 - Demis, Panagiotis A1 - Dorneanu, Bogdan A1 - Klymenko, Oleksiy A1 - Arellano-Garcia, Harvey T1 - A Methodology for Global Sensitivity Analysis for the Operation of Distributed Energy Systems Using a Two-Stage Approach T2 - 2019 AIChE Annual Meeting N2 - Optimisation-based models are often employed for the design and operation of distributed energy systems (DES). A two-stage approach often involves the optimisation of the design of a distributed energy system for a specified location or scale, and the subsequent optimisation of the operational model based on the structure recommended by the design model. The structure includes what types of generation and storage technologies should be used in the operation, related capacities and sizes, and potential locations. Often, both design and operational models are deterministic in nature, as either past or fictitious data is fed into the models to minimise an objective function such as the total cost or environmental impact due to carbon emissions. Consequently, the operational models encounter challenges when real-time data is fed, as time-variant input variables such as electricity demand, heating demand and solar insolation can be deemed uncertain. These variables could have unexpected and significant impacts on the total costs involved with the operation of distributed energy systems, leading to sub-optimality or even infeasibilities. Identifying these input variables, quantifying their uncertainties (which are then described in the models), and evaluating the influence of these variables on the outputs can lead to the design of more robust models. Such models can then be used to design and operate optimal distributed energy systems. This paper presents a novel methodology for using global sensitivity analysis (GSA) on an operational optimisation-based model of a distributed energy system. The operational model also utilises Model Predictive Control (MPC) rolling horizon concepts (as done by [1]) to determine hourly total operational costs. The paper also addresses how some challenges and limitations encountered in the operational model can be attributed to the deterministic design model on which the structure of the operational model has been based. Furthermore, the research explores how the design can be improved to support more robust operation. Another novel aspect of this paper highlights the use of the optimisation tool GAMS alongside SobolGSA, a global sensitivity analysis software [2]. This software uses the variance-based Sobol method to generate N samples and perform global sensitivity analysis, allowing users to understand how variations in the inputs can influence the outputs, whilst accounting for the different combinations of the uncertain parameters without varying one uncertain parameter at a time. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/373ah-methodology-global-sensitivity-analysis-operation-distributed-energy-systems-using-two-stage SN - 978-0-8169-1112-7 ER - TY - GEN A1 - Mechleri, Evgenia A1 - Shafiei, Zarif A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Klymenko, Oleksiy T1 - A Blockchain Model for Residential Distributed Energy Resources Networks T2 - 2019 AIChE Annual Meeting N2 - The energy production landscape is reshaped by distributed energy resources (DERs) – photovoltaic (PV) panels, combined heat and power (CHP), wind turbines (WT), fuel cells or battery storage systems, to name just a few [1]. Microgrids, collections of units or DERs that are locally controlled, close to the consumption point and cooperating with each other and the centralised grid [2], allow for the reduction in energy losses compared to traditional generation due to the close proximity to end users. Due to its volatility, the integration of this non-controllable generation poses severe challenges to the current energy system and ensuring a reliable balance of energy becomes an increasingly demanding task [3]. The optimal design and scheduling of the DERs and subsequent microgrid is of high importance in order to increase the reliability and determine their effectiveness in reducing losses, emissions and costs compared to conventional generation so that they may be implemented at faster rates to reduce global emissions and fossil fuel usage. In distributed energy systems, individual users typically have flexible tariffs while they also have the capability not only to use, but also to store and trade electric power. Direct transactions schemes can save money for end users, generate revenues for producers, reduce transmission losses and promote the use of renewable energy [4]. But it must be a robust, efficient and low-cost trading system to handle the rapid changes of information and value in the system. The blockchain technology can fulfil these requirements by enabling the implementation of optimal energy management strategies through distributed databases. Since its introduction as the underlying technology of Bitcoin, the blockchain technology has emerged from its use as a verification mechanism for cryptocurrencies and heads to a broader field of applications. Blockchain-based systems are basically a combination of a distributed ledger, a decentralised consensus mechanism, and cryptographic security measures [5]. More precisely, it allows the resolution of conflicts and dismantles information asymmetries by providing transparent and valid records of past transactions that cannot be altered retrospectively [6]. With the help of specific algorithms and applications, multiple operations can be performed automatically on the blockchain, using this information together with information from the Internet or the real world (e.g. on whether, energy pricing, etc.). Furthermore, smart contracts can be implemented between the nodes of the microgrid. This paper introduces a model for the implementation of a blockchain and smart contracts into the scheduling of a residential DER network. The blockchain is implemented in terms of energy rather than voltages [7], to allow for the decentralised operation of the microgrid without a centralised microgrid aggregator. Thus, the model will minimise only the operational cost. Furthermore, the DER network model is improved by the addition of more detailed transmission losses and costs within the microgrid and between the microgrid and the national grid. The resulted energy flows are stored and information on the availability/demand are exchanged between the network nodes. To appropriately compensate the DER operators in the microgrid for their services and to charge the consumers for withdrawals, nodal clearing prices are determined and implemented through smart contracts. The resulting MILP model minimises the overall investment and operating costs of the system. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/373ai-blockchain-model-residential-distributed-energy-resources-networks SN - 978-0-8169-1112-7 ER - TY - GEN A1 - Miah, Sayeef A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia A1 - Arellano-Garcia, Harvey T1 - Multi-Objective Design Optimisation of a Distributed Energy System through 3E (economic, environmental and exergy) Analysis T2 - 2020 Virtual AIChE Annual Meeting, November 20, 2020 N2 - To facilitate the commitments of reducing greenhouse gas emissions will require the utilisation of renewable energy resources, as well as shifting away from a centralised generation. Distributed energy systems (DESs) are a promising alternative to conventional centralised layouts. Thus, there is a need for the development of models able to optimally design DES which show savings in cost as well as having a low carbon impact. Current literature focuses on the design optimisation of a DES through economical and environmental cost minimisation [1-4]. However, these two criteria alone do not show the complete picture and do not satisfy the long-term sustainability priorities. The inclusion of exergy analysis allows for the satisfaction of this criteria through the rational use of energy resources. The use of exergy analysis within DESs was first studied by [5], with a multiobjective approach whereby cost and exergy efficiency are considered. The novelty of this paper is twofold. The first is the investigation of exergy DES design optimisation through a multiobjective approach whilst considering the economic and environmental cost, thus making this work the first to simultaneously minimise three objective functions in the context of DES. Y1 - 2020 UR - https://www.aiche.org/academy/conferences/aiche-annual-meeting/2020/proceeding/paper/340o-multi-objective-design-optimisation-distributed-energy-system-through-3e-economic-environmental SN - 978-0-8169-1114-1 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Heshmat, Mohamed A1 - Mohamed, Abdelrahim A1 - Ruan, Hang A1 - Xiao, Pei A1 - Gao, Yang A1 - Arellano-Garcia, Harvey T1 - Stepping Towards the Industrial Sixth Sense T2 - AIChE Annual Meeting, November 20, 2020 N2 - Industry 4.0 is transforming chemical processes into complex, smart cyber-physical systems, by the addition of elements such as smart sensors, Internet of Things, big data analytics or cloud computing. Modern engineering systems and manufacturing processes are operating in highly dynamic environments, and exhibiting scale, structure and behaviour complexity. Under these conditions, plant operators find it extremely difficult to manage all the information available, infer the desired conditions of the plant and take timely decisions to handle abnormal operation1. Human beings acquire information from the surroundings through sensory receptors for vision, sound, smell, touch, and taste, the Five Senses. The sensory stimulus is converted to electrical signals as nerve impulse data communicated with the brain. When one or more senses fail, the humans are able to re-establish communication and improve the other senses to protect from incoming dangers. Furthermore, a mechanism of ‘reasoning’ has been developed during evolution, which enable analysis of present data and generation of a vision of the future, which might be called the Sixth Sense. As industrial processes are already equipped with five senses: ‘hearing’ from acoustic sensors, ‘smelling’ from gas and liquid sensors, ‘seeing’ from camera, ‘touching’ from vibration sensors and ‘tasting’ from composition monitors, the Sixth Sense could be achieved by forming a sensing network which is self-adaptive and self-repairing, carrying out deep-thinking analysis with even limited data, and predicting the sequence of events via integrated system modelling. This contribution introduces the development of an intelligent monitoring and control framework for chemical process, integrating the advantages of Industry 4.0 technologies, cooperative control and fault detection via wireless sensor networks. Y1 - 2020 UR - https://www.aiche.org/academy/videos/conference-presentations/stepping-towards-industrial-sixth-sense SN - 978-0-8169-1114-1 ER - TY - GEN A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Use of Multistage Optimal Control Principles for Novel Design and Implementation of Classical Controllers T2 - AIChE Annual Meeting N2 - Classical controllers, such as Proportional-Integral (PI) and Proportional-Integral Derivative (PID) controllers, are the most long-established and widely used in industry. Various methods for tuning these types of controllers exist (Ziegler et al., 1942; Blondin et al., 2018; Do et al., 2021), and up to this point, there is no fruitful avenue to improve their performance. In this contribution, a new approach for PI and PID controller implementation, based on a Multistage Optimal Control (MSOCP) approach is introduced. Our approach incorporates path and end-point constraints during its controller tuning phase, as well as parameter and disturbance uncertainty. The proposed framework is applied for different case studies and is able to reject any disturbances introduced to the examined systems, with or without uncertainty, satisfies end-point constraints and exhibits quicker response for switching steady states, compared to classical methods. Other aspects of controller design and incorporation within industrial process models, as related to using rigorous optimization methodologies and implementations, will further be highlighted within the context of the PI and PID controllers. Y1 - 2021 UR - https://aiche.confex.com/aiche/2021/meetingapp.cgi/Paper/630692 ER - TY - GEN A1 - Sohail, Norman A1 - Riedel, Ramona A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques T2 - Membranes N2 - The membrane bioreactor (MBR) is an efficient technology for the treatment of municipal and industrial wastewater for the last two decades. It is a single stage process with smaller footprints and a higher removal efficiency of organic compounds compared with the conventional activated sludge process. However, the major drawback of the MBR is membrane biofouling which decreases the life span of the membrane and automatically increases the operational cost. This review is exploring different anti-biofouling techniques of the state-of-the-art, i.e., quorum quenching (QQ) and model-based approaches. The former is a relatively recent strategy used to mitigate biofouling. It disrupts the cell-to-cell communication of bacteria responsible for biofouling in the sludge. For example, the two strains of bacteria Rhodococcus sp. BH4 and Pseudomonas putida are very effective in the disruption of quorum sensing (QS). Thus, they are recognized as useful QQ bacteria. Furthermore, the model-based anti-fouling strategies are also very promising in preventing biofouling at very early stages of initialization. Nevertheless, biofouling is an extremely complex phenomenon and the influence of various parameters whether physical or biological on its development is not completely understood. Advancing digital technologies, combined with novel Big Data analytics and optimization techniques offer great opportunities for creating intelligent systems that can effectively address the challenges of MBR biofouling. KW - Membrane bioreactor (MBR) KW - quorum sensing (QS) KW - quorum quenching (QQ) KW - moving bed biofilm reactor (MBBR) KW - moving bed biofilm membrane reactor (MBBMR) KW - model-based anti-fouling strategies Y1 - 2023 UR - https://www.mdpi.com/2077-0375/13/2/217 U6 - https://doi.org/10.3390/membranes13020217 SN - 2077-0375 VL - 13 IS - 2 ER - TY - GEN A1 - Mappas, Vassileios A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Automated Control Loop Selection Via Multistage Optimal Control Formulation and Nonlinear Programming T2 - Chemical Engineering Research and Design N2 - In this work, a novel approach based on the multistage optimal control formulation of the control loop selection problem is introduced. Currently, state-of-the-art approaches for controller loop design have been focused on data that yield only the pairings between input-output variables, and are not able to incorporate path and end-point constraints. Thus, they only produce the optimal loops for control purposes, without the simultaneous consideration of their optimal tuning. This formulation overcomes these drawbacks by producing an automated integrated solution for the task of control loop design, which also obviates the need for any form of combinatorial optimisastion to be used. To illustrate the procedure, as well as the advantages of the proposed scheme, different practical case studies are discussed and the results compared with those obtained with standard controller loop selection methods and their tuning. The results of the proposed approach show improved performance over previous methodologies found in the literature. Furthermore, the framework is extended to the selection of the control loops that must obey path and end-point constraints imposed by the underlying dynamical process. This task is usually difficult for classical methods, which violate them or exhibit underdamped response in some cases. KW - control loop selection KW - controller tuning KW - feasible path approach KW - multistage integer nonlinear optimal control problem (MSINOCP) KW - dynamic constraints Y1 - 2023 U6 - https://doi.org/10.1016/j.cherd.2023.05.041 SN - 1744-3563 VL - 195 SP - 76 EP - 95 ER - TY - GEN A1 - Straub, Adrian A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Towards a novel concept for solid energy storage T2 - Computer Aided Chemical Engineering N2 - In this contribution, the model-based development of a novel process concept for the storage and release of ammonia in solids is proposed. The concept is validated by means of the Aspen Plus® process simulator. As a promising prospect, Hexaaminenickel(II) chloride is selected. After a preparative stage, the process can cycle between the storage and release of energy. The process is split in a reaction and a separation section, in such a way that the same equipment is used for both storage and release steps. Sensitivity analysis and design parameter optimization are used to determine key process parameters. The operation ranges from standard conditions (25 °C and 1 atm) to temperatures not higher than 120 °C. Moreover, the simulation results show that it is possible to store over 50% of the base material in form of ammonia, equivalent to almost 10 wt.% hydrogen, placing the concept within the specific system targets set by the U.S. Department of Energy. KW - Process design KW - Process modelling KW - Aspen Plus Y1 - 2023 U6 - https://doi.org/10.1016/B978-0-443-15274-0.50472-8 SN - 1570-7946 VL - Vol. 52 SP - 2965 EP - 2970 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Miah, Sayeef A1 - Mechleri, Evgenia A1 - Arellano-Garcia, Harvey T1 - Multiobjective optimization of distributed energy systems design through 3E (economic, environmental and exergy) analysis T2 - Computer Aided Chemical Engineering N2 - Distributed energy systems (DES) are promising alternative to conventional centralized generation, with multiple financial incentives in many parts of the world. Current approaches focus on the design optimization of a DES through economic and environmental cost minimization. However, these two criteria alone do not satisfy long-term sustainability priorities of the system. The novelty of this paper is the simultaneous investigation of economic, environmental and exergetic criteria in the modelling of DES through the two most commonly used solution methodologies for solving multi-objective optimization problems – the weighted sum and the epsilon-constraint methods. Out of the set of Pareto optimal solutions, a best-compromised solution is chosen using the fuzzy-based method. Numerical results reveal reduction of around 93% and 89-91% in environmental and primary exergy input, respectively. KW - Multiobjective optimization KW - Distributed energy systems KW - Exergy KW - Mixed-integer linear programming KW - Fuzzy-based methods Y1 - 2023 U6 - https://doi.org/10.1016/B978-0-443-15274-0.50473-X SN - 1570-7946 VL - Vol. 52 SP - 2971 EP - 2976 ER - TY - GEN A1 - Zhang, Sushen A1 - Vassiliadis, Vassilios S. A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Hierarchical multi-scale parametric optimization of deep neural networks T2 - Applied Intelligence N2 - Traditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the quantification of sensitivity for each neuron in a network presents a significant challenge. In this article, a selective method for calculating neuron sensitivity in layers of neurons concerning network output is proposed. This approach incorporates scaling factors that facilitate the evaluation and comparison of neuron importance. Additionally, a hierarchical multi-scale optimization framework is proposed, where layers with high-importance neurons are selectively optimized. Unlike the traditional backpropagation method that optimizes the whole network at once, this alternative approach focuses on optimizing the more important layers. This paper provides fundamental theoretical analysis and motivating case study results for the proposed neural network treatment. The framework is shown to be effective in network optimization when applied to simulated and UCI Machine Learning Repository datasets. This alternative training generates local minima close to or even better than those obtained with the backpropagation method, utilizing the same starting points for comparative purposes within a multi-start optimization procedure. Moreover, the proposed approach is observed to be more efficient for large-scale DNNs. These results validate the proposed algorithmic framework as a rigorous and robust new optimization methodology for training (fitting) neural networks to input/output data series of any given system. KW - Deep neural networks KW - Hierarchical multi-scale search KW - Scaling factor KW - Sensitivity analysis KW - Finite difference KW - Automatic differentiation Y1 - 2023 U6 - https://doi.org/10.1007/s10489-023-04745-8 SN - 1573-7497 VL - 53 IS - 21 SP - 24963 EP - 24990 ER - TY - GEN A1 - Jafari, Mitra A1 - Safdar, Muddasar A1 - Dorneanu, Bogdan A1 - Gonzalez-Castaño, Miriam A1 - Arellano-Garcia, Harvey T1 - Green and sustainable fuel from syngas via the Fischer-Tropsch synthesis process: Bifunctional cobalt-based catalysts T2 - 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology N2 - This paper reviews and compares state-of-the-art cobalt-based catalysts and catalytic systems used to produce green and sustainable fuels using FTS. Being focused on comparing the effect of the catalyst formulation and synthesis method, the reactor type and operating parameters, as well as the quality of the obtained fuels, the aim is to identify the research gaps between these relevant research areas concerning production of green and sustainable fuels. Y1 - 2023 UR - https://dechema.converia.de/frontend/index.php?page_id=15565&additions_conferenceschedule_action=detail&additions_conferenceschedule_controller=paperList&pid=44228&hash=be231d3139d7d89da32b1610b7a0d1af3770c06640f246348e3ca8cfa7dd324a ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Masham, Elliot A1 - Keykha, Mina A1 - Mechleri, Evgenia A1 - Cole, Rosanna A1 - Arellano-Garcia, Harvey T1 - Assessment of centralised and localised ice cream supply chains using neighbourhood flow configuration models T2 - Supply Chain Analytics N2 - Traditional food supply chains are often centralised and global in nature, entailing substantial resource consumption. However, in the face of growing demand for sustainability, this strategy faces significant challenges. Adoption of localised supply chains is deemed a more sustainable option, yet its efficacy requires verification. Supply chain analytics methodologies provide invaluable tools to guide decisions regarding inventory management, demand forecasting and distribution optimisation. These solutions not only enhance facilitate operational efficiency, but also pave the way for cost reduction, further aligning with sustainability objectives. This research introduces a novel decision-making approach anchored in mixed integer linear programming (MILP) and neighbourhood flow models defined in cellular automata to compare the environmental benefits and vulnerability to disruption of these two chain configurations. Additionally, a comprehensive cost analysis is integrated to assess the economic feasibility of incorporating layout changes that enhance supply chain sustainability. The proposed framework is applied on an ice cream supply chain across England over a one-year timeframe. The findings indicate the superiority of the localised configuration in terms of economic benefits, leading to savings exceeding £ 1 million, alongside important reductions in environmental impact. However, in terms of resilience, the traditional configuration remains superior in three out of the four examined scenarios. KW - supply chain management KW - flow configuration model KW - ice cream KW - Mixed-integer linear programming Y1 - 2023 UR - https://www.sciencedirect.com/science/article/pii/S2949863523000420 U6 - https://doi.org/10.1016/j.sca.2023.100043 VL - Vol. 4 ER - TY - GEN A1 - Cunha Cordeiro, José Luiz A1 - Safdar, Muddasar A1 - Aquino, Gabrielle S. A1 - Silva, Jefferson S. A1 - Paff, Jessica S. A1 - Valverde Pontes, Karen A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Mascarenhas, Artur José T1 - Sustainable hydrogen production via biogas reforming over NiO-MxOy - Al2O3 catalysts (M = Na, K, Ca and Mg) T2 - 22 Congreso Brasileiro de Catalise N2 - A sustainable way to generate hydrogen is through dry biogas reforming, which uses methane gas and carbon dioxide to produce hydrogen. This study reveals partial results of the dry reforming of biogas in NiO-MxOy-Al2O3 catalysts (M=Na, K, Ca and Mg). The CO2 conversion varied between 79% and 94%, the CH4 conversion between 58% and 75%, the H2/CO ratio between 0.98 and 1.15 and the H2 yield between 37% and 45%. These values ​​surpass literary references and the industrial catalyst, highlighting the promise of these materials for sustainable hydrogen production. The catalyst with Ca stood out due to its higher surface basicity, exhibiting the best conversion results and yield in H2. Y1 - 2023 UR - https://submissao.cbcat.sbcat.org.br/index.php/2023-cbcat/article/view/409 ER - TY - GEN A1 - Safdar, Muddasar A1 - Shezad, Nasir A1 - Dorneanu, Bogdan A1 - Jafari, Mitra A1 - Shashank Bhat, Sharvendu A1 - Akhtar, Farid A1 - Arellano-Garcia, Harvey T1 - Dry Reforming of Methane for the Syngas Production Catalyzed by Ni-doped Perovskites T2 - 15Th European Congress on Katakysis EUROPACAT2023 N2 - different perovskite-type supports considering ABO3 (such as A= Al, La with B=Ce and A=Mg, Mn with B=Zr) were prepared via the sol-gel method. Ni metal loading of 10 wt.% was deposited on prepared perovskite supports via the impregnation method. The catalysts were characterized using XRD and FTIR techniques. The DRM activity was carried out in a tubular reactor as described in our previous study [5]. The catalytic performance was assessed in the temperature range of 500–700 ◦C, CH4/CO2 = 1/1 and under GHSV of 12,000 h–1. Among the prepared catalysts, Ni-doped perovskite combination (i.e. A=Mg with B=Zr)O3-δ exhibited higher (CH4, CO2) conversion ca. (69, 59) percent and syngas yield of ca. (H2/CO =0.72) at 700 oC. This indicates that the magnesium zirconate perovskite catalyst established strong interfacial metal-support interaction, redox properties and surface basic sites that linked with good performance of the catalyst during DRM process. KW - Dry reforming of methane (DRM) KW - Ni-Perovskites KW - Syngas production KW - Greenhouse gases (GHGs) Y1 - 2023 ER - TY - GEN A1 - Arellano-Garcia, Harvey A1 - Safdar, Muddasar A1 - Shezad, Nasir A1 - Dorneanu, Bogdan A1 - Akhtar, Farid T1 - Synthesis and Characterizations of Ni-doped Perovskite-Type Oxides for Effective CO2 methanation T2 - 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology N2 - This work proposes Ni metal supported over rare earth-based emerging perovskite-type oxides as potential catalysts for the CO2 methanation. Presence of oxygen vacancies in perovskite-like materials enable them to exhibit higher catalytic activity. Furthermore, to tune the surface basicity, metal-support interaction and to enhance the activation of CO2, rare earth metals (La, Ce, etc.) are considered best candidates. Moreover, different perovskite-type supports (AxMnxO3, A= La, Ce) based on A-side substitution of rare earth metals were prepared with Ni metal loading of 10 wt.% via impregnation method. Y1 - 2023 UR - https://dechema.converia.de/frontend/index.php?page_id=13659&v=List&do=15&day=all&ses=9628# U6 - https://doi.org/10.5281/zenodo.10376612 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Nolasco, Eduardo A1 - Vassiliadis, Vassilios S. A1 - Arellano-Garcia, Harvey T1 - Quantum annealing for global optimization in Chemical Engineering T2 - Jahrestreffen "Prozess-, Apparate- und Anlagentechnik" - PAAT 2023, Frankfurt am Main N2 - Classical computing has experienced rapid growth in computational power, driven by the need to address increasingly complex industrial problems. The domain of global optimization plays a vital role in various applications, including optimal control, scheduling and assignment problems, or machine learning parameter selection. Currently, deterministic optimization techniques based on classical computing fail to deliver reasonable solutions within practical time constraints. Consequently, reliance on heuristic methods becomes common, albeit with no guarantee of solution quality. While ongoing algorithmic refinements lead to gradual enhancements in global optimization, they do little to address the fundamental issue of computational intractability. With the advent of quantum computing, a natural question arises: Can quantum methods offer advancements beyond classical approaches? Quantum annealing emerges as a promising subfield within quantum computing, necessitating the reformulation of problems as quadratic unconstrained binary optimization (QUBO) problems. In this contribution, a novel approach is introduced to transform relevant problems in Chemical Engineering into QUBO at two distinct levels of granularity. Subsequently, these problem systems are embedded within virtual quantum machines employing two different architectures. Additionally, a comparative analysis is performed, wherein the same problem is solved utilizing both classical global optimization methods based on metaheuristics and a hypothetical quantum annealer. The findings indicate that annealing-based solving methods exhibit the most potential, indicating their applicability to the transformed formulation Chemical Engineering problems. Y1 - 2023 UR - https://dechema.de/PAAT2023_Prg/_/__Progr_PAAT_2023_final.pdf SP - 15 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Jafari, Mitra A1 - Mbuya, Christel Olivier Lenge A1 - Arellano-Garcia, Harvey T1 - Towards an innovative process for the production of sustainable aviation fuels from biogenic raw materials T2 - Sustainable Aviation Fuels - Design, Production and Climate Impact N2 - Several thermochemical pathways (e.g., gasification, pyrolysis, hydrothermal carbonization, etc.) are available for the transformation of biomass into various products, including a gaseous stream from which syngas (CO+H2) can be obtained [1]. When combined with further downstream processing such as Fischer-Tropsch synthesis (FTS), this gas can be converted into renewable hydrocarbons, enabling the production of a wide range of high-value added products, including sustainable aviation fuels (SAF) [2]. This contribution introduces an ongoing research project that aims to overcome challenges related to the inefficiencies of the syngas-to-SAF pathway for the development of a new integrated process for the production of environmentally friendly aviation fuels from biogenic raw materials. The end product is expected to meet the requirements for a Jet A1 fuel according to the ASTM D 1655 standard [3]. For the achievement of this objective, industrial and academic partners work together for the development of new advanced catalytic systems to carry out the conversion of biomass-based syngas via FTS and hydrocracking in a single step using an interdisciplinary approach. The hybrid bifunctional catalysts produced are coated on microchannel reactors, and their performance compared with commercial catalysts available on the market. Subsequently, the reactor modules are manufactured using 3D printing and tested at pilot scale, and the SAF production via the innovative one-step process is integrated with the biomass conversion to syngas, for validation in a relevant industrial environment. Y1 - 2023 UR - https://www.we-heraeus-stiftung.de/fileadmin/Redaktion/PDF/Seminare/2023/789-booklet.pdf SP - 37 CY - Bad Honnef, Germany ER - TY - GEN A1 - Mechleri, Evgenia A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - A Model Predictive Control-Based Decision-Making Strategy for Residential Microgrids T2 - Eng N2 - This work presents the development of a decision-making strategy for fulfilling the power and heat demands of small residential neighborhoods. The decision on the optimal operation of a microgrid is based on the model predictive control (MPC) rolling horizon. In the design of the residential microgrid, the new approach different technologies, such as photovoltaic (PV) arrays, micro-combined heat and power (micro-CHP) units, conventional boilers and heat and electricity storage tanks are considered. Moreover, electricity transfer between the microgrid components and the national grid are possible. The MPC problem is formulated as a mixed integer linear programming (MILP) model. The proposed novel approach is applied to two case studies: one without electricity storage, and one integrated microgrid with electricity storage. The results show the benefits of considering the integrated microgrid, as well as the advantage of including electricity storage. Y1 - 2022 UR - https://www.mdpi.com/2673-4117/3/1/9 U6 - https://doi.org/10.3390/eng3010009 SN - 2673-4117 VL - 3 IS - 1 SP - 100 EP - 115 ER - TY - GEN A1 - Campos, Jean C. A1 - Manrique-Silupú, José A1 - Ipanaqué, William A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - Mechanistic modelling for thrips incidence in organic banana T2 - Computer Aided Chemical Engineering N2 - This contribution introduces a data acquisition and modelling framework for the prediction of banana pests’ incidence. An IoT sensors-based system collects weather and micro-climate variables, such as temperature, relative humidity, and wind speed, which are uploaded in real time to a cloud storage space. The incidence of the red rust thrips (Chaetanaphothrips signipennis) is collected “manually” by periodic inspection. The mathematical model is adapted from population growth functions and a model of insect species development and allows predictions to be made at various time intervals with an accuracy greater than 80%, improving decision-making capacity for agro-producers and enabling the improvement of pest management actions. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323958790500461?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-95879-0.50046-1 SN - 1570-7946 VL - 51 SP - 271 EP - 276 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Heinzelmann, Norbert A1 - Schnitzlein, Klaus A1 - Arellano-Garcia, Harvey T1 - BasMo - An interactive approach to modelling of trickle bed reactors T2 - Jahrestreffen der "Prozess-, Apparate- und Anlagentechnik", 21.–22. November 2022, Frankfurt am Main N2 - The trickle bed reactor (TBR), in which gas and liquid flow downward through a packed bed to undergo chemical reactions, is a frequently used solution for industrial multiphase exothermic catalytic reactions (e.g., hydrogenation, oxidation, etc.) due to flexibility and simplicity of operation and large annual throughput (Tan et al., 2021). They have significant advantages with respect to other solutions, but they also show complex behaviour, with uncertainties in catalyst heterogeneity, packing, fluid flow, and transport parameters, resulting in its modelling being highly challenging (Azarpour et al., 2021). In this contribution, the development of an interactive toolbox for the simulation of TBRs, based on the work of Schwidder & Schnitzlein (2012) is introduced. The implementation uses a modular and flexible setup, mirroring the multiscale nature of the phenomena tacking place in the reactor, from large scale of the reactor to the medium and low scale of the particle bed, fluid flow, as well as fluid-solid and fluid-fluid interactions, including chemical reactions. The toolbox enables implementation of complex geometries of the catalyst particles, enabled by a novel representation of the surface mesh. Validation using experimental data shows that the model is able to reliably predict the performance of the catalytic TBR. Y1 - 2022 UR - https://dechema.de/PAAT2022_Themen/_/_1_Programm_PAAT_2022_ezl.pdf ER - TY - GEN A1 - Arellano-Garcia, Harvey A1 - Barz, Tilman A1 - Dorneanu, Bogdan A1 - Vassiliadis, Vassilios S. T1 - Real-time feasibility of nonlinear model predictive control for semi-batch reactors subject to uncertainty and disturbances T2 - Computers & Chemical Engineering N2 - This paper presents two nonlinear model predictive control based methods for solving closed-loop stochastic dynamic optimisation problems, ensuring both robustness and feasibility with respect to state output constraints. The first one is a new deterministic approach, using the wait-and-see strategy. The key idea is to specifically anticipate violation of output hard-constraints, which are strongly affected by instantaneous disturbances, by backing off of their bounds along the moving horizon. The second method is a stochastic approach to solve nonlinear chance-constrained dynamic optimisation problems under uncertainties. The key aspect is the explicit consideration of the stochastic properties of both exogenous and endogenous uncertainties in the problem formulation (here-and-now strategy). The approach considers a nonlinear relation between uncertain inputs and the constrained state outputs. The performance of the proposed methodologies is assessed via an application to a semi-batch reactor under safety constraints, involving strongly exothermic reactions. Y1 - 2020 UR - https://www.sciencedirect.com/science/article/pii/S0098135419303333?via%3Dihub U6 - https://doi.org/10.1016/j.compchemeng.2019.106529 SN - 1873-4375 SN - 0098-1354 VL - 133 ER - TY - GEN A1 - Gehring, Nicole A1 - Dorneanu, Bogdan A1 - Manrique-Silupú, José A1 - Ipanaqué, William A1 - Arellano-Garcia, Harvey T1 - Circular Economy in Banana Cultivation T2 - Computer Aided Chemical Engineering N2 - This paper examines the most important approaches that could be applied for the introduction of circular economy strategies for the banana production process in the region of Piura (Peru). Based on this, a framework for an optimized economic cycle that is able to conserve resources and minimize the capital investments of farmers, while simultaneously increasing their production, can be created. Challenges and potential solutions are discussed for the production stages. Y1 - 2020 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780128233771502627?via%3Dihub U6 - https://doi.org/10.1016/B978-0-12-823377-1.50262-7 SN - 1570-7946 VL - 48 SP - 1567 EP - 1572 ER - TY - GEN A1 - González-Castaño, Miriam A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - The Reverse Water Gas Shift Reaction: A Process Systems Engineering Perspective T2 - Reaction Chemistry & Engineering N2 - The catalytic reduction of CO2 into value-added products has been considered a compelling solution for alleviating global warming and energy crises. The reverse water gas shift (RWGS) reaction plays a pivotal role among the various CO2 utilization approaches, due to the fact that it produces syngas, the building block of numerous conversion processes. Although a lot of work has been carried out towards the development of a RWGS process, ranging from efficient catalytic systems to reactor units, and even pilot scale processes, there is still a lack of understanding of the fundamental phenomena that take place at the various levels and scales of the process. This contribution presents the main solutions and remaining challenges for a structured, trans- and multidisciplinary framework in which catalysis engineering and process systems engineering can work together to incorporate understanding and methods from both sides, to accelerate the investigation, creation and operation of an efficient industrial CO2 conversion process based on the RWGS reaction. KW - Reverse water gas shift reaction KW - syngas production KW - catalysts KW - process engineering Y1 - 2021 U6 - https://doi.org/10.1039/D0RE00478B SN - 2058-9883 ER - TY - GEN A1 - Sidnell, Tim A1 - Dorneanu, Bogdan A1 - Mechleri, Evgenia A1 - Vassiliadis, Vassilios S. A1 - Arellano-Garcia, Harvey T1 - Effects of Dynamic Pricing on the Design and Operation of Distributed Energy Resource Networks T2 - Processes N2 - This paper presents a framework for the use of variable pricing to control electricity im-ported/exported to/from both fixed and unfixed residential distributed energy resource (DER) network designs. The framework shows that networks utilizing much of their own energy, and importing little from the national grid, are barely affected by dynamic import pricing, but are encouraged to sell more by dynamic export pricing. An increase in CO2 emissions per kWh of energy produced is observed for dynamic import and export, against a baseline configuration utilizing constant pricing. This is due to feed-in tariffs (FITs) that encourage CHP generation over lower-carbon technologies. Furthermore, batteries are shown to be expensive in systems receiving income from FITs and grid exports, but for the cases when they sell to/buy from the grid using dynamic pricing, their use in the networks becomes more economical. Keywords: distributed energy resource (DER); dynamic pricing; mixed-integer linear programming (MILP); renewable heat incentive (RHI); feed-in tariff (FIT); electricity storage in batteries. Y1 - 2021 UR - https://www.mdpi.com/2227-9717/9/8/1306 U6 - https://doi.org/https://doi.org/10.3390/pr9081306 SN - 2227-9717 VL - 9 IS - 8 ER - TY - GEN A1 - Alves Amorim, Ana Paula A1 - Dorneanu, Bogdan A1 - Valverde Pontes, Karen A1 - Arellano-Garcia, Harvey T1 - A framework for decision-making to encourage utilization of residential distributed energy systems in Brazil T2 - Computer Aided Chemical Engineering N2 - The Distributed Energy Systems (DES) or microgrid arose from the need to reduce greenhouse gases (GHG) emitted into the atmosphere by burning fossil fuels to generate energy. Reduction of energy losses, reconfiguration of the protection system and reduction of costs, and optimizing the configuration of these systems is recommended. Despite new research in literature, there is still a lack of optimization models that address the Brazilian reality. Therefore, the objective of this work is to introduce a decision-making framework for the design and operation of residential DES that takes into account the particularities of Brazil, based on mixed-integer programming models. The applicability of the framework is tested on a case study of a residential DES of 5 houses, located in Salvador, and used to compare scenarios pre- and post-COVID-19. The results show significant reduction in total annual cost and GHG emissions versus the base case without DES. This indicates that, although the country has a mostly “clean” energy matrix due to the use of hydroelectric plants, DES can enable improvement in residential electricity generation. KW - Distributeed energy systems KW - Microgrid KW - Mixed-integer non-linear programming KW - Net metering Y1 - 2023 U6 - https://doi.org/10.1016/B978-0-443-15274-0.50481-9 SN - 1570-7946 VL - Vol. 52 SP - 3019 EP - 3024 ER - TY - GEN A1 - Ruan, Hang A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey A1 - Xiao, Pei A1 - Zhang, Li T1 - Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes T2 - IEEE Access N2 - This paper investigates the challenging fault prediction problem in process industries that adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the emerging developments of industrial internet of things (IIoT) and Industry 4.0. Particularly, we developed an end-to-end deep learning approach based on a large volume of real-time sensory data collected from a chemical plant equipped with wireless sensors. Firstly, a novel recursive architecture with multi-lookback inputs is proposed to perform autoregression on imbalanced time-series data as a preliminary prediction. In this process, a novel learning algorithm named recursive gradient descent (RGD) is developed for the proposed architecture to reduce cumulative prediction uncertainties. Subsequently, a classification model based on temporal convolutions over multiple channels with decay effect is proposed to perform multi-class classification for fault root cause identification and localization. The overall network is named the cumulative uncertainty reduction network (CURNet), for its superior capacity in reducing prediction uncertainties accumulated over multiple prediction steps. Performance evaluations show that CURNet is able to achieve superior performance especially in terms of fault prediction recall and fault type classification accuracy, compared to the existing techniques. Y1 - 2022 UR - https://ieeexplore.ieee.org/document/9684389 U6 - https://doi.org/10.1109/ACCESS.2022.3144333 SN - 2169-3536 VL - 10 SP - 10867 EP - 10879 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Heshmat, Mohamed A1 - Mohamed, Abdelrahim A1 - Arellano-Garcia, Harvey T1 - Monitoring of smart chemical processes: A Sixth Sense approach T2 - Computer Aided Chemical Engineering N2 - This paper introduces the development of an intelligent monitoring and control framework for chemical processes, integrating the advantages of technologies such as Industry 4.0, cooperative control or fault detection via wireless sensor networks. The system described is able to detect faults using information on the process’ structure and behaviour, information on the equipment and expert knowledge. Its integration with the monitoring system facilitates the detection and optimisation of controller actions. The results indicate that the proposed approach achieves high fault detection accuracy based on plant measurements, while the cooperative controller improves the operation of the process. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323851596502268?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-85159-6.50226-8 SN - 1570-7946 VL - 49 SP - 1357 EP - 1362 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Zhang, Sushen A1 - Ruan, Hang A1 - Heshmat, Mohamed A1 - Chen, Ruijuan A1 - Vassiliadis, Vassilios S. A1 - Arellano-Garcia, Harvey T1 - Big data and machine learning: A roadmap towards smart plants T2 - Frontiers of Engineering Management N2 - Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as the addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management. Y1 - 2022 UR - https://link.springer.com/article/10.1007/s42524-022-0218-0 U6 - https://doi.org/10.1007/s42524-022-0218-0 VL - 9 SP - 623 EP - 639 ER - TY - GEN A1 - Ketabchi, Elham A1 - Ramirez Reina, Tomas A1 - Dorneanu, Bogdan A1 - Arellano-Garcia, Harvey T1 - An identification approach to a reaction network for an ABE catalytic upgrade T2 - Computer Aided Chemical Engineering N2 - This contribution presents a kinetic study for the identification of the complex reaction mechanism occurring during the ABE upgrading, and the development of a kinetic model. Employing graph theory analysis, a directed bipartite graph is constructed to reduce the complexity of the reaction network, and the reaction rate constants and reaction orders are calculated using the initial rate method, followed by the calculation of the activation energy and frequency factor for an Arrhenius-type law. Subsequently, using general mass balancing a proposed mathematical model is produced to determine the apparent reaction rates, which are successfully in line with the experimental results. Y1 - 2021 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323885065501029?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-88506-5.50102-9 SN - 1570-7946 VL - 50 SP - 643 EP - 648 ER - TY - GEN A1 - Mohamed, Abdelrahim A1 - Ruan, Hang A1 - Heshmat Hassan Abdelwahab, Mohamed A1 - Dorneanu, Bogdan A1 - Xiao, Pei A1 - Arellano-Garcia, Harvey A1 - Gao, Yang A1 - Tafazolli, Rahim T1 - An Inter-Disciplinary Modelling Approach in Industrial 5G/6G and Machine Learning Era T2 - 2020 IEEE International Conference on Communications Workshops (ICC Workshops) N2 - Recently, the fifth-generation (5G) cellular system has been standardised. As opposed to legacy cellular systems geared towards broadband services, the 5G system identifies key use cases for ultra-reliable and low latency communications (URLLC) and massive machine-type communications (mMTC). These intrinsic 5G capabilities enable promising sensor-based vertical applications and services such as industrial process automation. The latter includes autonomous fault detection and prediction, optimised operations and proactive control. Such applications enable equipping industrial plants with a sixth sense (6S) for optimised operations and fault avoidance. In this direction, we introduce an inter-disciplinary approach integrating wireless sensor networks with machine learning-enabled industrial plants to build a step towards developing this 6S technology. We develop a modular-based system that can be adapted to the vertical-specific elements. Without loss of generalisation, exemplary use cases are developed and presented including a fault detection/prediction scheme, and a sensor density-based boundary between orthogonal and non-orthogonal transmissions. The proposed schemes and modelling approach are implemented in a real chemical plant for testing purposes, and a high fault detection and prediction accuracy is achieved coupled with optimised sensor density analysis. Y1 - 2020 UR - https://ieeexplore.ieee.org/document/9145434 SN - 978-1-7281-7440-2 SN - 978-1-7281-7441-9 U6 - https://doi.org/10.1109/ICCWorkshops49005.2020.9145434 SN - 2474-9133 SP - 1 EP - 6 CY - Dublin, Ireland ER - TY - GEN A1 - Merino, Manuel A1 - Carrasco, Laura A1 - Dorneanu, Bogdan A1 - Manrique, Jose A1 - Menzhausen, Robert A1 - Arellano-Garcia, Harvey T1 - Control Strategies for a Vapour Compression Refrigeration System Used in Mango Exports: An Alternative to Traditional on-Off Controllers T2 - AIChE Annual Meeting N2 - ropical fruits are important products on the global market. The change to a healthier nutrition, the development of new products and great availability led to a rise in their consumption during the last decade [1-2]. Due to their perishable nature they are stored at lower temperature. To achieve a rapid and efficient decrease in product temperature, refrigeration systems are employed, using vapor compression refrigeration plants. They consist of four main components: the compressor, the condenser, the expansion valve and the evaporator. Within the system a refrigerant is circulating. Though designed to satisfy maximum load, these plants usually work at part-load for much of their life, generally regulated by on/off cycles of the compressor, working at nominal frequency of 50 Hz [3]. The high cost involved in developing cold storage or controlled atmosphere storage is a pressing problem in several developing countries [4]. This contribution presents development and comparison of various strategies for the control of a refrigeration plant used for fruit cooling. The starting point is a model of the plant which is able to simulate both the chamber and the fruit temperature. The model is based on energy balances for each section of the refrigeration system and the fruits. Y1 - 2020 UR - https://www.aiche.org/academy/conferences/aiche-annual-meeting/2020/proceeding/paper/340c-control-strategies-vapour-compression-refrigeration-system-used-mango-exports-alternative SN - 978-0-8169-1114-1 ER - TY - GEN A1 - Arellano-Garcia, Harvey A1 - El Bari, Hassan A1 - Kalibe Fanezoune, Casimir A1 - Dorneanu, Bogdan A1 - Majozi, Thokozani A1 - Elhenawy, Yasser A1 - Bayssi, Oussama A1 - Hirt, Ayoub A1 - Peixinho, Jorge A1 - Dhahak, Asma A1 - Gadalla, Mamdouh A. A1 - Khashaba, Nourhan H. A1 - Ashour, Fatma T1 - Catalytic Fast Pyrolysis of Lignocellulosic Biomass: Recent Advances and Comprehensive Overview T2 - Journal of Analytical and Applied Pyrolysis N2 - Using biomass as a renewable resource to produce biofuels and high-value chemicals through fast pyrolysis offers significant application value and wide market possibilities, especially in light of the current energy and environmental constraints. Bio-oil from fast-pyrolysis has various conveniences over raw biomass, including simpler transportation and storage and a higher energy density. The catalytic fast pyrolysis (CFP) is a complex technology which is affected by several parameters, mainly the biomass type, composition, and the interaction between components, process operation, catalysts, reactor types, and production scale or pre-treatment techniques. Nevertheless, due to its complicated makeup, high water and oxygen presence, low heating value, unstable nature, elevated viscosity, corrosiveness, and insolubility within conventional fuels, crude bio-oil has drawbacks. In this context, catalysts are added to reactor to decrease activation energy, substitute the output composition, and create valuable compounds and higher-grade fuels. The study aim is to explore the suitability of lignocellulosic biomasses as an alternative feedstock in CFP for the optimization of bio-oil production. Furthermore, we provide an up-to-date review of the challenges in bio-oil production from CFP, including the factors and parameters that affect its production and the effect of used catalysis on its quality and yield. In addition, this work describes the advanced upgrading methods and applications used for products from CFP, the modeling and simulation of the CFP process, and the application of life cycle assessment. The complicated fluid dynamics and heat transfer mechanisms that take place during the pyrolysis process have been better understood due to the use of CFD modeling in studies on biomass fast pyrolysis. Zeolites have been reported for their superior performance in bio-oil upgrading. Indeed, Zeolites as catalyses have demonstrated significant catalytic effects in boosting dehydration and cracking process, resulting in the production of final liquid products with elevated H/C ratios and small C/O ratios. Combining ex-situ and in-situ catalytic pyrolysis can leverage the benefits of both approaches. Recent studies recommend more and more the development of pyrolysis-based bio-refinery processes where these approaches are combined in an optimal way, considering sustainable and circular approaches. KW - Catalytic fast pyrolysis KW - Lignocellulosic Biomass KW - Bio-Oil KW - Modelling Y1 - 2024 U6 - https://doi.org/10.1016/j.jaap.2024.106390 SN - 0165-2370 VL - Vol. 178 ER -