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• The separation of MEK/IPA/n-heptane by triple column pressure-swing distillation is studied. • The heat integration scheme and dynamic control strategy of triple column pressure-swing distillation are studied. • The full heat integration process has good ISE value and minimum TAC. In this paper, an economical and robust triple column pressure-swing distillation (TCPSD) process for the separation of butanone/isopropanol/n-heptane (MEK/IPA/n-heptane) ternary azeotrope is studied. The pressure sensibility of MEK/IPA/n-heptane is qualitatively analyzed by residue curve map (RCM), which demonstrated that the economical design for separating the ternary mixture via TCPSD is feasible. The TCPSD process was optimized by the minimum total annual cost (TAC) by using the sequential iterative algorithm, meanwhile, the heat integration is investigated to reduce the energy consumption. The results showed that the partial heat integration can reduce TAC by 11.89%, and the full heat integration can reduce TAC by 39.97% by parameter optimization. The dynamic control strategies that can resist 20% of feed disturbances were proposed for the conventional process, partial heat integration and full heat integration, respectively. The designed and optimized full heat integration scheme is found to be an optimal scheme with good anti-disturbance performance and the best economic performance. This study provides a new train of thought for the separation of MEK/IPA/n-heptane and has meaningful practical application value. [Display omitted] [ABSTRACT FROM AUTHOR]

• An intelligent control strategy without the composition analyzer is proposed. • Variable selection based on correlation improves BPNN prediction accuracy. • The universality of the intelligent control strategy is proved by two PSD cases. In this paper, an intelligent control strategy based on back propagation neural network (BPNN) is proposed for product composition control in pressure-swing distillation (PSD) processes. A data-driven intelligent controller based on BPNN was combined with PID control instead of traditional composition controllers to avoid the problem that composition is difficult to measure online in real-time. The intelligent controllers are used to predict temperature set point in composition- temperature cascade control by using the process variables easy to measure, e.g., reboiler duty, thus avoiding composition measurement. The critical variables for output prediction are analyzed by correlation analysis to present the relationship between the output variables and input variables, then to train highly correlated variables by BPNN. Two typical triple-columns PSD processes, i.e., Ethanol/THF/Water and ACN/IPA/Water, were used to verify the reliability and accuracy of the intelligent controllers under ±20% of feed flow and composition disturbances. Results demonstrated that the proposed intelligent control strategy presents good dynamic performance without the composition analyzer. This study is significant in improving dynamic performance and solving practical application problems by combining the traditional PID control and data-driven intelligent control. [Display omitted] [ABSTRACT FROM AUTHOR]

• A digital twin model of pharmaceutical workshop with 3D visualization for solvent purification and recovery was designed. • The purity of product and solvent was effectively improved by multi-objective genetic algorithm and system evaluation. • The effectiveness of digital twin in predictive capability using CNN-LSTM regression model was demonstrated. Digital twin is to make full use of the physical model, sensor update, operation history and other data to integrate multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process, and to complete the full life cycle process of the corresponding physical equipment in the virtual space. In this paper, digital twin technology was used to simulate the production process of a pharmaceutical workshop, thus realizing solvent recovery by an azeotrope system separation process. The separation was optimized by multi-objective genetic algorithm. separation process, wherein the polynomial curve fitting and entropy-weighted TOPSIS data evaluation algorithms were combined to optimize the operating parameters, then the solvent purity and yield reach 99.96 % and 77.9 %, respectively, which is much higher than the corporate requirements. In addition, the effectiveness of the digital twin in providing data-driven prediction capability was verified by a CNN-LSTM-based regression prediction model, and the CNN-LSTM model has an accuracy of more than 98.9 % for all prediction results. Finally, a 3D visualization model was built using the Unity engine, enabling researchers to visualize and explore changes in key metrics during the separation process. This study can provide some guidance for digital transformation and intelligent development in pharmaceutical industry. [Display omitted] [ABSTRACT FROM AUTHOR]

• Heat integration and dynamic control of reactive pressure-swing distillation for separating tetrahydrofuran/ethanol/water were investigated. • Three generalized and robust control strategies that can resist ±20% feed disturbances were proposed for RPSD. • The TAC and exhaust emission (CO 2 , SO 2, and NO) were greatly reduced while the dynamic control performance was improved. In this paper, heat integration and dynamic control of reactive pressure-swing distillation (RPSD) for separating tetrahydrofuran/ethanol/water was investigated from the aspects of energy consumption, environmental protection and practical applications. By exploring the heat exchange between separating tetrahydrofuran and ethanol distillation columns, the full heat integration process (FHI) was designed. Robust control of heat and non-heat integrated processes was realized based on the dual temperature control structure. All processes were resistant to ±20% feed flow and composition disturbances. Finally, these processes were compared from three aspects: economy, environment and dynamic performance. The results show that compared with the non-heat integration process, the FHI process can reduce the total annual cost by 24.14%, the CO 2 emission by 20.21% and the SO 2 and NO emission by 34.38%, meanwhile, having the best dynamic performance. This study is valuable and instructive in promoting the integrated optimal and control design of the RPSD process. [Display omitted] [ABSTRACT FROM AUTHOR]

• Dynamic control schemes for two azeotropic distillation processes are developed. • The influence of makeup on purity of product under different disturbances is explored. • IAE is used to evaluate the performance of dynamic control structure. A novel acetonitrile/water azeotropic distillation scheme shows obvious economic advantages. It is of great significance to study the dynamic controllability of this process. In this work, the dynamic control structures of the non heat-integration and full heat-integration azeotropic distillation are studied. The control ability of the proposed control structures is tested by adding flow disturbance and composition disturbance. For the non heat-integration scheme, the conventional PID control structure and composition controller is used to achieve effective control when a disturbance occurs. For the full heat-integration scheme, the temperature difference control scheme and the composition controller are used to handle disturbances. This control structure reduces the stabilization time and makes the purity of the product return to the set value. The effect of different makeup flow on the purity of acetonitrile under different disturbances is discussed. Moreover, two control strategies are synthetically analyzed and compared by calculating integral of squared error. Considering economy and controllability, it is concluded that control strategy of heat-integrated azeotropic distillation has more advantages. This study has certain guiding value for the research of chemical dynamic control. [Display omitted] [ABSTRACT FROM AUTHOR]