Ionic liquid (IL)/polyimide (PI) composite membranes demonstrate promise for use in CO₂ separation applications. However, few studies have focused on the microscopic mechanism of CO₂ in these composite systems, which is important information for designing new membranes. In this work, a series of systems of CO₂ in 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide composited with 4,4-(hexafluoroisopropylidene) diphthalic anhydride (6FDA)-based PI, 6FDA-2,3,5,6-tetramethyl-1,4-phenylene-diamine, at different IL concentrations were investigated by all-atom molecular dynamics simulation. The formation of IL regions in PI was found, and the IL regions gradually became continuous channels with increasing IL concentrations. The analysis of the radial distribution functions and hydrogen bond numbers demonstrated that PI had a stronger interaction with cations than anions. However, the hydrogen bonds among PI chains were destroyed by the addition of IL, which was favorable for transporting CO₂. Furthermore, the self-diffusion coefficient and free energy barrier suggested that the diffusion coefficient of CO₂ decreased with increasing IL concentrations up to 35 wt-% due to the decrease of the fractional free volume of the composite membrane. However, the CO₂ self-diffusion coefficients increased when the IL contents were higher than 35 wt-%, which was attributed to the formation of continuous IL domain that benefitted the transportation of CO₂.

Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemical-based products. The life cycles of chemical products involve the procedures of conceptual product designs, experimental investigations, sustainable manufactures through appropriate chemical processes and waste disposals. During these periods, one of the most important keys is the molecular property prediction models associating molecular structures with product properties. In this paper, a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions, such as activity coefficient, and so forth. The workflow of framework consists of three steps. In the first step, a database is created for collections of basic molecular information; in the second step, quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties (pseudo experimental data), which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step. The whole framework has been carried out within a molecular property prediction toolbox. Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.

Direct application of bio-oil from fast pyrolysis as a fuel has remained a challenge due to its undesirable attributes such as low heating value, high viscosity, high corrosiveness and storage instability. Solvent addition is a simple method for circumventing these disadvantages to allow further processing and storage. In this work, computer-aided molecular design tools were developed to design optimal solvents to upgrade bio-oil whilst having low environmental impact. Firstly, target solvent requirements were translated into measurable physical properties. As different property prediction models consist different levels of structural information, molecular signature descriptor was used as a common platform to formulate the design problem. Because of the differences in the required structural information of different property prediction models, signatures of different heights were needed in formulating the design problem. Due to the combinatorial nature of higher-order signatures, the complexity of a computer-aided molecular design problem increases with the height of signatures. Thus, a multi-stage framework was developed by developing consistency rules that restrict the number of higher-order signatures. Finally, phase stability analysis was conducted to evaluate the stability of the solvent-oil blend. As a result, optimal solvents that improve the solvent-oil blend properties while displaying low environmental impact were identified.

Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations, so that solvability becomes a practical issue. Additional constraints from technical, economic, environmental, and safety considerations may further limit the feasible solution space beyond the convergence requirement. A priori, the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice. To support the exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning models has recently been proposed. However, that approach only considers the exploration of the convergent domain and ignores additional constraints. In this paper, we present an improvement which particularly takes the fulfillment of constraints into account. We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

Solvent-based post-combustion capture technologies have great potential for CO₂ mitigation in traditional coal-fired power plants. Modelling and simulation provide a low-cost opportunity to evaluate performances and guide flexible operation. Composed by a series of partial differential equations, first-principle post-combustion capture models are computationally expensive, which limits their use in real time process simulation and control. In this study, we propose a first-principle approach to develop the basic structure of a reduced-order model and then the dominant factor is used to fit properties and simplify the chemical and physical process, based on which a universal and hybrid post-combustion capture model is established. Model output at steady state and trend at dynamic state are validated using experimental data obtained from the literature. Then, impacts of liquid-to-gas ratio, reboiler power, desorber pressure, tower height and their combination on the absorption and desorption effects are analyzed. Results indicate that tower height should be designed in conjunction with the flue gas flow, and the gas-liquid ratio can be optimized to reduce the reboiler power under a certain capture target.

Simulation is besides experimentation the major method for designing, analyzing and optimizing chemical processes. The ability of simulations to reflect real process behavior strongly depends on model quality. Validation and adaption of process models are usually based on available plant data. Using such a model in various simulation and optimization studies can support the process designer in his task. Beneath steady state models there is also a growing demand for dynamic models either to adapt faster to changing conditions or to reflect batch operation. In this contribution challenges of extending an existing decision support framework for steady state models to dynamic models will be discussed and the resulting opportunities will be demonstrated for distillation and reactor examples.

To study the dynamic behavior of a process, time-resolved data are collected at different time instants during each of a series of experiments, which are usually designed with the design of experiments or the design of dynamic experiments methodologies. For utilizing such time-resolved data to model the dynamic behavior, dynamic response surface methodology (DRSM), a data-driven modeling method, has been proposed. Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used in several of previous publications, and Lasso regression, which is newly incorporated in this paper for the estimation of DRSM models. Here, we show that both approaches yield similarly accurate models, while the computational time of Lasso is on average two magnitude smaller. Two case studies are performed to show the advantages of the proposed method. In the first case study, where the concentrations of different species are modeled directly, DRSM method provides more accurate models compared to the models in the literature. The second case study, where the reaction extents are modeled instead of the species concentrations, illustrates the versatility of the DRSM methodology. Therefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety of organic synthesis datasets.

Advanced model-based control strategies, e.g., model predictive control, can offer superior control of key process variables for multiple-input multiple-output systems. The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization. This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control. To showcase this approach, five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system. This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges. These controllers also had reasonable per-iteration times of ca. 0.1 s. This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which, in the face of process uncertainties or modelling limitations, allow rapid and stable control over wider operating ranges.

The conceptual process design of novel bioprocesses in biorefinery setups is an important task, which remains yet challenging due to several limitations. We propose a novel framework incorporating superstructure optimization and simulation-based optimization synergistically. In this context, several approaches for superstructure optimization based on different surrogate models can be deployed. By means of a case study, the framework is introduced and validated, and the different superstructure optimization approaches are benchmarked. The results indicate that even though surrogate-based optimization approaches alleviate the underlying computational issues, there remains a potential issue regarding their validation. The development of appropriate surrogate models, comprising the selection of surrogate type, sampling type, and size for training and cross-validation sets, are essential factors. Regarding this aspect, satisfactory validation metrics do not ensure a successful outcome from its embedded use in an optimization problem. Furthermore, the framework’s synergistic effects by sequentially performing superstructure optimization to determine candidate process topologies and simulation-based optimization to consolidate the process design under uncertainty offer an alternative and promising approach. These findings invite for a critical assessment of surrogate-based optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions.

Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

An energy-efficient triple-column extractive distillation process is developed for recovering tetrahydrofuran and ethyl acetate from industrial effluent. The process development follows a rigorous hierarchical design procedure that involves entrainer design, thermodynamic analysis, process design and optimization, and heat integration. The computer-aided molecular design method is firstly used to find promising entrainer candidates and the best one is determined via rigorous thermodynamic analysis. Subsequently, the direct and indirect triple-column extractive distillation processes are proposed in the conceptual design step. These two extractive distillation processes are then optimized by employing an improved genetic algorithm. Finally, heat integration is performed to further reduce the process energy consumption. The results indicate that the indirect extractive distillation process with heat integration shows the highest performance in terms of the process economics.

Dimethyl carbonate is an eco-friendly essential chemical that can be sustainably produced from CO₂, which is available from carbon capture activities or can even be captured from the air. The rapid increase in dimethyl carbonate demand is driven by the fast growth of polycarbonates, solvent, pharmaceutical, and lithium-ion battery industries. Dimethyl carbonate can be produced from CO₂ through various chemical pathways, but the most convenient route reported is the indirect alcoholysis of urea. Previous research used techniques such as heat integration and reactive distillation to reduce the energy use and costs, but the use of an excess of methanol in the trans-esterification step led to an energy intensive extractive distillation required to break the dimethyl carbonate-methanol azeotrope. This work shows that the production of dimethyl carbonate by indirect alcoholysis of urea can be improved by using an excess of propylene carbonate (instead of an excess of methanol), a neat feat that we showed it requires only 2.64 kW·h·kg^–1 dimethyl carbonate in a reaction-separation-recycle process, and a reactive distillation column that effectively replaces two conventional distillation columns and the reactor for dimethyl carbonate synthesis. Therefore, less equipment is required, the methanol-dimethyl carbonate azeotrope does not need to be recycled, and the overall savings are higher. Moreover, we propose the use of a reactive distillation column in a heat integrated process to obtain high purity dimethyl carbonate (>99.8 wt-%). The energy requirement is reduced by heat integration to just 1.25 kW·h·kg^–1 dimethyl carbonate, which is about 52% lower than the reaction-separation-recycle process. To benefit from the energy savings, the dynamics and control of the process are provided for ±10% changes in the nominal rate of 32 ktpy dimethyl carbonate, and for uncertainties in reaction kinetics.