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A systematic and efficient method for the rigorous design of complex chemical processes is significant in the chemical industry. In this paper, a superstructure-based optimization approach for the rigorous and simultaneous design of reaction and separation processes using generalized disjunctive programming (GDP) models is presented. In the reactor network, disjunctions for conditional reactors are introduced where the balance and reaction kinetic equations are applied only if the reactor is selected. Based on the proposed reactor disjunctions, two different reactor superstructures are developed and employed. In addition, the GDP representation of distillation columns is used to model the separation network. The reliability and efficiency of the proposed optimization method are demonstrated on two case studies, i.e., one cyclohexane oxidation process and one benzene chlorination process. The flowsheet structure and process-unit operating conditions are simultaneously optimized to minimize the total annual cost of the processes.

A new method for integrated ionic liquid (IL) and absorption process design is proposed where a rigorous rate-based process model is used to incorporate absorption thermodynamics and kinetics. Different types of models including group contribution models and thermodynamic models are employed to predict the process-relevant physical, kinetic, and thermodynamic (gas solubility) properties of ILs. Combining the property models with process models, the integrated IL and process design problem is formulated as an MINLP optimization problem. Unfortunately, due to the model complexity, the problem is prone to convergence failure. To lower the computational difficulty, tractable surrogate models are used to replace the complex thermodynamic models while maintaining the prediction accuracy. This provides an opportunity to find the global optimum for the integrated design problem. A pre-combustion carbon capture case study is provided to demonstrate the applicability of the method. The obtained global optimum saves 14.8% cost compared to the Selexol process.

Extractive distillation is a widely accepted and commercialized process for separating azeotropic mixtures compared to conventional distillation. The search for high-performing solvents, or entrainers, needed in extractive distillation is a challenging task. The heuristic guideline or experiment based method for the screening of entrainers is usually not very efficient and limited to the existing, well-known solvents. In this contribution, we propose a multi-stage theoretical framework to design solvents for extractive distillation. A multiobjective optimization based computer-aided molecular design (MOO-CAMD) method is developed and used to find a list of Pareto-optimal solvents. In the MOO-CAMD method, two important solvent properties (i.e., selectivity and capacity) that determine the extractive distillation efficiency are simultaneously optimized. The next step involves a further screening of the Pareto-optimal solvents by performing rigorous thermodynamic calculation and analysis. Finally, for each of the remaining solvents, the extractive distillation process is optimally designed and the best candidate showing the highest process performance is ultimately identified. The overall design framework is illustrated through an example of the n-hexane and methanol separation.

An integrated metal–organic framework (MOF) and pressure/vacuum swing adsorption (P/VSA) process design framework is presented for gas separation. It consists of two steps: adsorbent descriptor optimization, and MOF matching. In the first step, MOFs are represented as a large set of chemical and geometric descriptors from which the most influential ones are selected via a multistep screening method and treated as design variables. The valid design space of the selected descriptors is confined using a tailored classifier model and logic constraints. Based on collected adsorption isotherms of 471 different MOFs, data‐driven isotherm models are developed. Combining the design space, isotherms, and four‐step P/VSA process models, an integrated MOF and P/VSA process design problem is formulated. MOF descriptors and process operating conditions are optimized to maximize the process performance. The obtained optimal descriptors and isotherms can be used to guide the discovery of high‐performance MOFs in a subsequent MOF matching step. This article addresses the first descriptor optimization step exemplified by propene/propane separation.

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To identify optimal ionic liquids (ILs) for CO2 capture, an efficient computer-aided IL design (CAILD) approach is desired. The traditional CAILD methods usually combine an equation of state with the UNIFAC-IL model to calculate gas solubility, which is computationally expensive and sometimes cannot give quantitatively satisfying results. In this contribution, a new CAILD approach is presented for the optimal design of ILs for CO2 capture, where mathematically simple and reliable data-driven models are applied to predict CO2 solubility. The IL design problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with the objective of maximizing the CO2 solubility of ILs under prespecified conditions. Global optimal solutions are successfully obtained due to model simplicity. Moreover, to prevent misleading results led by poor extrapolability of data-driven models, multiple data-driven models are trained from the same experimental solubility database. These models are then adopted in different batches of the MINLP formulation. For each batch, the optimization problem is solved to generate top IL candidates. Only the ILs that repeatedly appear in different batches are considered as reliable solutions falling into the validity domain of the data-driven models. Such a new strategy can effectively enhance design reliability. The CO2 capture performance of the designed ILs is finally confirmed using density functional theory calculations. The applicability of the proposed method is illustrated in a case study of post-combustion carbon capture.

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