Designing products and processes is a fundamental aspect of engineering that significantly impacts society and the world. Chemical process design aims to create more efficient and sustainable production processes that consume fewer resources and emit less pollution. Mathematical models that accurately describe process behavior are necessary to make informed and responsible decisions. However, as processes become more complex, purely symbolic formulations may be inadequate, and simulations using tailored computer code become necessary. The decision‐making process in optimal design requires a procedure for choosing the best option while complying with the system’s constraints, for which task optimization approaches are well suited. This doctoral thesis focuses on black‐box optimization problems that arise when using process simulators in optimal process design tasks and assesses the potential of derivative‐free, metaheuristics, and surrogate‐based optimization approaches. The optimal design of natural gas liquefaction processes is the case study of this research. To overcome numerical issues from black‐box problems, the first work of this doctoral thesis consisted of using the globally convergent Nelder‐Mead simplex method to the optimal process design problem. The second work introduced surrogate models to assist the search towards the global optimum of the black‐box problem and an adaptive sampling scheme comprising the optimization of an acquisition function with metaheuristics. Kriging as surrogate models to the simulation‐optimization problems are computationally cheaper and effective predictors suitable for global search. The third work aims to overcome the limitations of acquisition function optimization and the use of metaheuristics. The proposed comprehensive mathematical notation of the surrogate optimization problem was readily implementable in algebraic modeling language software. The presented framework includes kriging models of the objective and constraint functions, an adaptive sampling procedure, a heuristic for stopping criteria, and a readily solvable surrogate optimization problem with mathematical programming. The success of the surrogate‐based optimization framework relies on the kriging models’ prediction accuracy regarding the underlying, simulation‐based functions. The fourth publication extends the previous work to multi‐objective black‐box optimization problems. It applies the ε constraint method to transform the multi‐objective surrogate optimization problem into a sequence of single‐objective ones. The ε‐constrained surrogate optimization problems are implemented automatically in algebraic modeling language software and solved using a gradient‐based, state‐of‐the‐art solver. The fifth publication is application-driven and focuses on identifying the most suitable mixed‐refrigerant refrigeration technology for natural gas liquefaction in terms of energy consumption and costs. The study investigates five natural gas liquefaction processes using particle swarm optimization and concludes that there are flaws in the expected relationships between process complexity, energy consumption, and total annualized costs. In conclusion, the research conducted in this doctoral thesis demonstrates the importance and capabilities of using optimization to process simulators. The work presented here highlights the potential of surrogate‐based optimization approaches to significantly reduce the computational cost and guide the search in black‐box optimization problems with chemical process simulators embedded. Overall, this doctoral thesis contributes to developing optimization strategies for complex chemical processes that are essential for addressing some of the current most pressing environmental and social challenges. The methods and insights presented in this work can help engineers and scientists design more sustainable and efficient processes, contributing to a better future for all.

One of the most important factors of the prosperity in each company is people -- its employees. It is well known, that when the employees are unsatisfied, they don't use so much effort in their work or in a worse case they loose interest to stay in the company and they start to seek for another one. Remarkable influence on stabilization of employees in a company certainly has their successful adaptation to the new employment. This diploma work focuses on an analysis of the adapting process in the company Siemens Ltd. Using a questionnaire there were inefficient components of the contemporary programme for adaptation of new employees discovered and recommendations for the management of Siemens Ltd. were proposed.

A fundamental problem of microwave (MW) thermal processing of materials is the intrinsic non-uniformity of the resulting internal heating pattern. This project proposes a general technique to solve this problem by using comprehensive numerical modeling to determine the optimal process guaranteeing uniformity. The distinctive features of the approach are the use of an original concept of uniformity for MW-induced temperature fields and pulsed MW energy as a mechanism for achieving uniformity of heating. The mathematical model used to represent MW heating describes two component physical processes: electromagnetic wave propagation and heat diffusion. A numerical solution for the corresponding boundary value problem is obtained using an appropriate iterative framework in which each sub-problem is solved independently by applying the 3D FDTD method. Given a specific MW heating system and load configuration, the optimization problem is to find the experiment which minimizes the time required to raise the minimum temperature of the load to a prescribed goal temperature while maintaining the maximum temperature below a prescribed threshold. The characteristics of the system which most dramatically influence the internal heating pattern, when changed, are identified through extensive modeling, and are subsequently chosen as the design variables in the related optimization. Pulsing MW power is also incorporated into the optimization to allow heat diffusion to affect cold spots not addressed by the heating controlled by the design variables. The developed optimization algorithm proceeds at each time-step by choosing the values of design variables which produce the most uniform heating pattern. Uniformity is measured as the average squared temperature deviation corresponding to all distinct neighboring pairs of FDTD cells representing the load. The algorithm is implemented as a collection of MATLAB scripts producing a description of the optimal MW heating process along with the final 3D temperature field. We demonstrate that CAD of a practical applicator providing uniform heating is reduced to the determination of suitable design variables and their incorporation into the optimization process. Although uniformity cannot be attained using“static" MW heating, it is achievable by applying an appropriate pulsing regime. The functionality of the proposed optimization is illustrated by computational experiments which show that time-to-uniformity can be reduced, compared to the pulsing regime, by up to an order of magnitude.