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Processes 2022, 10, x FOR PEER REVIEW 13 of 19 Processes 2022, 10, 812 13 of 19 of the VPSA process, which explained the effects of the P/F ratio, adsorption time and desorption pressure on product purity, recovery and process energy consumption. Fiigure5..FllowcchhaarrttooffththeeBBBDDmeeththooddfoforrththeePPSASApprorcoecsesscictietdedbybyZZhahnagngeteatla. l[.7[17]1. ]R. eRaedaadpatpedted with permission from Ref. [71]. Copyright 2021 Elsevier. with permission from Ref. [71]. Copyright 2021 Elsevier. With the devellopmenttooffcocommppuutetre,rm, matahtehmemataictsicasnadnodthoetrhderisdcipsclinpelisnaensdatnhde tphreogpreos-s gorfecsosmofpcuotminpguptionwgepro,awretirfi,cairatli-fincitaell-lingtenllciege(AncIe)t(eAcIh)nteoclhognyolhoagsymhasdmesaidgensifiigcnainfitcparnotgprreos-s and breakthroughs in recent years, especially in the fields of image vision, automatic gress and breakthroughs in recent years, especially in the fields of image vision, automatic driving, scene prediction and robotics [73]. The industry 4.0 era combines physical and driving, scene prediction and robotics [73]. The industry 4.0 era combines physical and digital with advanced technologies such as big data, artificial intelligence, machine learning, digital with advanced technologies such as big data, artificial intelligence, machine learn- etc., providing a new approach to the process industry that is more comprehensive, inter- ing, etc., providing a new approach to the process industry that is more comprehensive, connected and smarter [33,74]. AI-based optimization algorithms are more widely used interconnected and smarter [33,74]. AI-based optimization algorithms are more widely than traditional optimization algorithms. Several studies have used novel metaheuristic used than traditional optimization algorithms. Several studies have used novel metaheu- and AI-based optimization algorithms, including the genetic algorithm (GA) and parti- ristic and AI-based optimization algorithms, including the genetic algorithm (GA) and cle swarm optimization (PSO) [75,76]. Perez [66] combined experiments with a genetic particle swarm optimization (PSO) [75,76]. Perez [66] combined experiments with a ge- algorithm to obtain Pareto curves for the multi-objective optimization of the enrichment netic algorithm to obtain Pareto curves for the multi-objective optimization of the enrich- of CO from a mixture of 15% CO + 85% N . The performance indicators, such as CO 2222 ment of CO2 from a mixture of 15% CO2 + 85% N2. The performance indicators, such as purity and recovery, the transients of temperatures, outlet flow and composition, showed CO2 purity and recovery, the transients of temperatures, outlet flow and composition, an excellent match with the experiments. Compared with ordinary genetic algorithms, the showed an excellent match with the experiments. Compared with ordinary genetic algo- second-generation nondominated sorting genetic algorithm (NSGA-II) (1) has excellent rithms, the second-generation nondominated sorting genetic algorithm (NSGA-II) (1) has robustness to multi-objective optimization; (2) can avoid local minima caused by initial excellent robustness to multi-objective optimization; (2) can avoid local minima caused by value guessing, and can provide the Pareto optimal solution set; (3) is capable of easily par- initial value guessing, and can provide the Pareto optimal solution set; (3) is capable of allelizing processing on multi-core computing devices, greatly reducing the running time; easily parallelizing processing on multi-core computing devices, greatly reducing the run- (4) can be used to know the experimental design and process design; (5) can be combined ning time; (4) can be used to know the experimental design and process design; (5) can be with the ANN surrogate model, when it can meet optimization processes requiring a large combined with the ANN surrogate model, when it can meet optimization processes re- number of computing samples without additional computing power consumption. quiring a large number of computing samples without additional computing power con- sumption. 3.2. Control Strategy In an actual PSA process, performance indicators will deviate from the set value or fail due to the performance degradation of the device, fluctuations in upstream and 3.2. Control Strategy In an actual PSA process, performance indicators will deviate from the set value or downstream processes, changes in external conditions, and the setting of process goals. fail due to the performance degradation of the device, fluctuations in upstream and down- Therefore, it is necessary to apply a real-time control to the PSA process and adjust the op- stream processes, changes in external conditions, and the setting of process goals. There- erating parameters to keep the process performance at the set state. At present, the control fore, it is necessary to apply a real-time control to the PSA process and adjust the operating strategies used in the industry are mainly divided into proportional-integral-differential parameters to keep the process performance at the set state. At present, the control strat- control (PID) and advanced process control (APC) [77,78]. PID is a control strategy that egies used in the industry are mainly divided into proportional-integral-differential combines three control laws: proportional (P), integral (I) and differential (D). It determines the current control input based on the deviation of the current and past output measure- ments of the process from the set value. Xing et al. [16] used a PID method for controlling two cases: in case 1, the CO2 concentration of the feed decreased from 15% to 12%, and inPDF Image | Numerical Research on the Pressure Swing Adsorption Process
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