Refine
Document Type
Way of publication
- Open Access (7)
Language
- English (57)
Keywords
Institute
BTU
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Optimal design and operation of distributed energy resources systems for residential neighbourhoods
(2021)
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.
A framework for intelligent monitoring and control of chemical processes with multi-agent systems
(2020)
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.
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.
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.
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.
A model-based approach for the prediction of banana rust thrips incidence from atmospheric variables
(2022)
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.
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.
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.
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.