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.

The cryogenic carbon capture™ (CCC) process provides energy- and cost-efficient carbon capture and can be configured to provide an energy storage system using an open-loop natural gas (NG) refrigeration system, which is called energy storing cryogenic carbon capture (CCC-ES™). This investigation focuses on the transient operation and especially on the dynamic response of this energy storage system and explores its efficiency, effectiveness, design, and operation. This investigation included four tasks.The first task explores the steady-state design of four different natural gas liquefaction processes simulated by Aspen HYSYS. These processes differ from traditional LNG process in that the CCC process vaporizes the LNG and the cold vapors return through the LNG heat exchangers, exchanging sensible heat with the incoming flows. The comparisons include costs and energy performance with individually optimized processes, each operating at three operating conditions: energy storage, energy recovery, and balanced operation. The second task examines steady-state and transient models and optimization of natural gas liquefaction using Aspen HYSYS. Steady-state exergy and heat exchanger efficiency analyses characterize the performance of several potential systems. Transient analyses of the optimal steady-state model produced most of the results discussed here. The third task explores transient Aspen HYSYS modeling and optimization of two natural gas liquefaction processes and identifies the rate-limiting process components during load variations. Novel flowrate variations included in this investigation drive transient responses of all units, especially compressors and heat exchangers. Model-predictive controls (MPC) effectively manages such heat exchangers and compares favorably with results using traditional controls. The last task shows how an unprocessed natural gas (NG) pretreatment system can remove more than 90% of the CO2 from NG with CCC technology using Aspen Plus simulations and experimental data. This task shows how CCC-based technology can treat NG streams to prepare them for LNG use. Data from an experimental bench-scale apparatus verify simulation results. Simulated results on carbon (CO2) capture qualitatively and quantitatively agree with experimental results as a function of feedstock properties.