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This thesis summarizes the author’s developments of combustion models and multi-objective optimization methods for gasoline and diesel engines. The combustion models belong to the family of zero-dimensional stochastic reactor models introduced in the 1990s to improve the prediction of emissions with detailed chemistry in partially stirred reactors.
The first part introduces the fundamentals of the physical and chemical models describing the combustion process. As a novelty, k−ε turbulence models were implemented in the stochastic reactor model to predict the turbulent time and length scales in gasoline and diesel engines. This development allowed an improvement of the models for convective heat transfer, fuel evaporation, gas exchange across the valves, turbulent flame propagation and crevice flow, which depend on the turbulent time and length scales.
In the second part, the multi-objective optimization platform for automatic training of the stochastic reactor model is presented. The optimization method considers multiple operating points to find a set of model parameters that predict performance and emissions over the entire engine map. The Non-domination Sorting Genetic Algorithm II is combined with the stochastic reactor model and response surface models to find the best Pareto front. Multi-criteria decision making is used to select the best designs from the Pareto front.
Finally, the third part of this thesis deals with the validation of the stochastic reactor model and the multi-objective optimization platform. For this purpose, experiments of two single-cylinder research engines with spark ignition, one passenger car engine with compression ignition and one heavy duty engine with compression ignition are used. For the spark ignition engines, a set of model parameters was found that predicts well the power and emissions over the whole engine map. The calculated turbulent kinetic energy, dissipation, and angular momentum follow the trends of the three-dimensional computational fluid dynamic simulations to a good approximation for various operating points. For the two compression ignition engines, the prediction of combustion progress and nitrogen oxide emissions are in good agreement with the experiments. Larger discrepancies were found for the prediction of carbon monoxide and unburned hydrocarbon. Optimization of the soot model parameters improves the prediction of soot mass for operating points throughout the engine map.
Reduction of energy consumption has increasingly come into sharp focus in the chemical process industry. This is of great value not only for existing plant but also for the development of new processes. Therefore, the challenge for process design engineers to develop an integrated chemical process that simultaneously satisfies economic and environmental objectives has increased considerably. Particularly, multi-objective optimization in the chemical industry has become increasingly popular during the last decade. The main problem lies, in selecting the alternative best design during decision making with multiple and often conflicting objectives. This thesis work presents a methodology for the multi-objective optimization of process design alternatives under economic and environmental objectives and also to establish the linkage between exergy and the environment. Four distillation units design alternatives with increasing level of heat integration were considered. Each design is analysed from exergy, potential environmental impact (PEI) and economic point of view. A non-dominated solution known as the “Pareto optimal solution” is generated for decision making. The thermodynamic efficiency indicates where exergy losses occur. The demand for industrial process heat by means of solar energy has generated much interest because it offers an innovative way to reduce operating cost and improve clean renewable electric power. Concentrated Solar Thermal Power (CSP) can provide solution to global energy problems within a relatively short time and is capable of contributing to carbon dioxide reduction, which is an important step towards zero emissions in the process industries. This work provides an overview of a simulation model to evaluate the environmental and economic performance of two case studies of solar thermal power plants. A methodology is presented to integrate solar thermal power plant into industrial processes and this is then compared with an existing hydrocarbon recovery (HCR) plant that depends on coal as its energy source. The two process design alternatives where simulated using the process simulator Aspen PlusTM. This thesis work also evaluates two types of power plants based on coal. The plants considered provide utility systems such as steam and electrical energy to the process plants. Exergy analysis was performed for each type of plant. The standard PEI calculation procedure has been modified for consideration of specific energy resources or power plants.
Nowadays industrial aerodynamic compressor design is based on mature computer programs developed during several decades. State of the art is to split the complex design process into subsequent design subtasks which are solved by different experts via time-consuming parameter studies. Isolated design of subproblems based on human intuition, however, will result in sub-optimal solutions only. Due to the increasing demand on higher aero engine performance and design cycle time reduction the aspects of process integration and automation as well as numerical optimization become more and more important in today’s aerodynamic compressor design. The intention of this work is to show how process integration and optimization can be used efficiently to support engineering design work in optimal solution finding. Since the aerodynamic compressor design is characterized by many design parameters, multiple constraints and contradicting objectives, multi-objective optimization is used to find Pareto-optimal solutions from which the design engineer can choose trade-offs for his particular design problem. The improvements in terms of process acceleration and design optimization are demonstrated for three selected, but typical industrial engineering design tasks required in three different design phases of the aerodynamic compressor design process, namely preliminary design, throughflow off-design, and blading procedure.