Distillation is an energy-intensive non-stationary process represented using non-linear model equations and involves multiple objectives. For such processes, data-based multi-objective optimization methods are more suitable compared to conventional non-linear optimization methods. Therefore, a surrogate-assisted multi-objective optimization (SAMOO) approach is developed by hybridizing an artificial neural network (ANN) and genetic algorithm (GA) to simultaneously minimize the annualized capital expenditure cost (ACAPEX) and annualized operational expenditure cost (AOC) for the methanol separation process. The approach is then extended for operational optimization to maximize methanol purity and minimize heat duty. The Pareto optimal fronts obtained using the data-based SAMOO approach are found to be very close to the optimization results obtained using the actual physics-based Aspen Plus model. The coupling of the genetic algorithm and ANN modeling in SAMOO approach reduces the computing time of optimization by ∽ 50% with nearly the same results as that of the physics-based model. 

Optimisation of methanol distillation using GA and neural network hybrid

Development of AI-based process controller of sour water treatment unit using deep reinforcement learning

Sour water stripping can treat the sour water produced by crude oil processing, which has the effect of environmental protection, energy saving and emission reduction. This paper aims to reduce energy consumption of the unit by strengthening process parameter optimization. Firstly, the basic model is established by utilizing Aspen Plus, and the optimal model is determined by comparative analysis of back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) models. Then, the sensitivity analysis of Sobol is used to select the operating variables that have a significant influence on the energy consumption of the sour water stripping system. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the operating conditions of the sour water stripping unit. The results show that the RBFNN model is more accurate than other models. Its network structure is 5-66-1, and the expected value has an approximately linear relationship with the output value. Through sensitivity analysis, it is found that each operating parameter has an impact on the sour water stripping process, which needs to be optimized by the PSO algorithm. After 210 iterations of the PSO algorithm, the optimal system energy consumption is obtained. In addition, the cold/hot feed ratio, sideline production position, tower bottom pressure, hot feed temperature, and cold feed temperature are 0.117, 18, 436 kPa, 146 °C, and 35 °C, respectively; the system energy consumption is 5.918 MW. Compared with value of 7.128 MW before optimization, the energy consumption of the system is greatly reduced by 16.97%, which shows that the energy-saving effect is very significant.

/pdf/optimization-of-sour-water-stripping-unit-using-artificial-17cdywle.pdf

Optimization of Sour Water Stripping Unit Using Artificial Neural Network–Particle Swarm Optimization Algorithm

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Optimization of Sour Water Stripping Unit Using Artificial Neural Network–Particle Swarm Optimization Algorithm