Search

[Display omitted] • A novel and energy-saving reactive pressure-swing distillation process was designed for separating the THF-methanol-water mixture. • Energy consumption was greatly reduced by multi-objective genetic algorithm and heat-integrated optimization. • The total annual cost of the designed two heat integration processes for the RPSD-B was greatly reduced, i.e., 45.9% and 52.2%, respectively. • The water in THF-methanol-water was fully utilized to react with ethylene oxide to generate by-product EG. The low-energy and low-cost process development and design has always been a hot topic and challenge in separating azeotropic systems in the world. In this paper, a novel, energy-saving reactive pressure-swing distillation (RPSD) was, for the first time, designed for separating the ternary mixture of tetrahydrofuran (THF)-methanol–water. In the reactive distillation column, the process of reacting water with ethylene oxide to produce ethylene glycol for the first time was used to separate THF-methanol–water mixture, then the residual mixtures were separated by two pressure-swing distillation columns. Two feasible RPSD separation processes were investigated in terms of thermodynamic modeling and T-xy phase diagram analysis to achieve optimal separation sequence. The two designed separation processes were optimized by a multi-objective genetic algorithm and heat integration to maximize economic benefit. Results demonstrated that, compared to the conventional pressure-swing distillation process, the total annual cost of the designed two heat-integrated processes based on the RPSD-B process were greatly reduced, i.e., 45.9% and 52.2%, respectively. This paper has important guiding significance for the separation and purification of multicomponent azeotropes. [ABSTRACT FROM AUTHOR]

• A novel soft senor model predictive control (SS-MPC) is proposed for the azeotropic distillation (AD) of the separation of DIPE/IPA/water mixture. • The time-delayed neural networks (TDNN) was used to improve the prediction of the soft sensor model. • The weights of the SS-MPC controller were optimized by the multi-objective genetic algorithm. • The proposed SS-MPC presents better dynamic performances than the classical PID control and avoids the use of composition analysis instrument. A novel advanced control scheme for azeotropic distillation (AD) shows superiority in improving product quality and economic benefit. A soft sensor model predictive control (SS-MPC) strategy was developed for the separation of the diisopropyl ether (DIPE)/ isopropyl alcohol (IPA)/water mixture via AD. The soft sensor model was built based on time-delayed neural networks (TDNN), while the weight of the SS-MPC controller was optimized by multi-objective genetic algorithm. Product purity of two distillation columns was controlled simultaneously by the proposed soft sensor model predictive controller, meanwhile, avoiding the measurement of composition in practice. The dynamic performance was compared with the conventional PID scheme and MPC scheme in the face of ±10 % feed disturbances. The results show that SS-MPC can better control the product purity than PID without using the composition detection instruments. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • A efficient and environmentally friendly extractant for separating toluene-methanol-water azeotrope. • Three feasible schemes were designed by changing solvent and optimized by multi-objective genetic algorithm. • The proposed double-column extractive distillation process with a decanter presents superior performance. • The TAC and CO 2 emissions were reduced by 33.1 % and 50.5 %, respectively, compared with the existing process. Cleaner production and sustainable development are still hot topics for modern industry. Developing green technology with low energy consumption, low pollution, and low cost has always been the common pursuit of chemical, petroleum, pharmaceutical, and other industries. This article proposes an environmentally friendly and efficient solvent, glycerol, for the separation of the ternary azeotrope of toluene-methanol-water. Three feasible extractive distillation schemes were designed and optimized, while the double-column extractive distillation process with a decanter demonstrated superior performance when glycerol was used as the solvent. Additionally, the multi-objective genetic algorithm was employed to optimize these separation processes and determine the optimal operating conditions, taking total annual cost (TAC), CO 2 emissions (E CO2), and separation efficiency (E ext) as target performance indicators to achieve the objectives of energy conservation, consumption reduction, and emission reduction. Results demonstrated that the newly proposed scheme can significantly reduce the total annual cost by up to 33.1 % and CO 2 emissions by 50.5 % compared with the existing separation process. This work provides a new insight to separate and purify the multiple systems by extractive distillation in industry. [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]

[Display omitted] • A novel adaptive neuro-fuzzy inference system and PID (ANFIS-PID) fusion control scheme was proposed for product composition control in extractive distillation. • ANFIS-PID fusion control was optimised and improved using the genetic algorithm (GA) and the particle swarm optimisation (PSO) algorithm separately. • The number of clusters (NC) as a key indicator was analysed to improve the prediction and control ability of ANFIS. • The proposed PSO-optimised ANFIS-PID fusion control scheme exhibited superior dynamic performance under ±20% feed disturbances. Real-time measurement and control of product composition in extractive distillation continues to remain a challenge; however, composition control has demonstrated the ability to stabilise control systems. In this study, a dynamic control scheme is proposed that uses an improved PID-fused adaptive neuro-fuzzy inference system (ANFIS); herein, ANFIS replaces composition controllers, improving the real-time performance of the control system. The ANFIS algorithm, however, suffers from problems such as easily falling into a local optimum solution. Therefore, the genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm are separately used to optimise the network parameters of ANFIS, and the impact of different number of clusters (NC) on the performance of the control scheme is evaluated. The control scheme of PSO-optimised PID-ANFIS exhibits superior dynamic performance compared to other control schemes; moreover, it does not require the use of component controllers and has a faster response time and smaller transient values. [ABSTRACT FROM AUTHOR]