Intelligent Optimization Design of Distillation Columns Using Surrogate Models Based on GA-BP

The design of distillation columns significantly impacts the economy, energy consumption, and environment of chemical processes. However, optimizing the design of distillation columns is a very challenging problem. In order to develop an intelligent technique to obtain the best design solution, improve design efficiency, and minimize reliance on experience in the design process, a design methodology based on the GA-BP model is proposed in this paper. Firstly, a distillation column surrogate model is established using the back propagation neural network technique based on the training data from the rigorous simulation, which covers all possible changes in feed conditions, operating conditions, and design parameters. The essence of this step is to turn the distillation design process from model-driven to data-driven. Secondly, the model takes the minimum TAC as the objective function and performs the optimization search using a Genetic Algorithm to obtain the design solution with the minimum TAC, in which a life-cycle assessment (LCA) model is incorporated to evaluate the obtained optimized design solution from both economic and environmental aspects. Finally, the feasibility of the proposed method is verified with a propylene distillation column as an example. The results show that the method has advantages in convergence speed without sacrificing accuracy and can obtain an improved design solution with reduced cost and environmental impact. Compared with the original design using rigorous simulation, the TAC is reduced by 6.1% and carbon emission by 27.13 kgCO2/t

Design Optimization and Carbon Footprint Analysis of an Electrodeionization System with Flexible Load Regulation

Thermal power plants will function as a flexible load regulation in a low-carbon grid, which requires operation adaption for the whole system. Energy transition in the electricity sector is the core to realizing carbon neutrality. The power grid will be gradually dominated by renewable energy, such as wind power and photovoltaic solar power. However, renewable energy has problems such as insufficient power supply and output fluctuation; thermal power will be required to regulate the peak load flexibly to meet demand. Therefore, the supply of boiler make-up water prepared by electrodeionization (EDI) in thermal power plants should also be flexibly changed. This study focused on the ultrapure water preparation system by EDI with variable flow rates. For an EDI system with a maximum ultrapure water capacity of 20 m3/h, the power consumption, annual cost, and carbon footprint of different designs are compared. The operation parameters were optimized based on the optimal cost design when the temporal demand of boiler make-up water is reduced to 75%, 50%, and 25%, respectively, considering thermal power as peak load regulation technology. The results showed that the optimized system could significantly reduce power consumption and carbon footprint by up to 30.21% and 30.30%, respectively. The proposed strategy is expected to be widely applied for design and operation optimization when considering the low-carbon but unstable energy system dominated by renewable energy. The carbon footprint could be a feasible optimization object to balance the greenhouse gas emissions from the module manufacturing and operation consumption