This study evaluates the effectiveness of a modelling and optimization methodology based on artificial neural networks and genetic algorithms in the prediction of the behaviour of an electrolysis process of active chlorine production from a synthetic saline effluent. Multilayer perceptrons feedforward neural networks were developed for the active chlorine production and energy consumption based on the following inputs: electrolysis time, current intensity, hydrochloric acid concentration, and chloride ion concentration. In order to diagnose and prevent the over‐fitting problem during the learning process, learning curves and the regularization factor were utilized. The trained ANN models were able to successfully predict the active chlorine production and energy consumption of the process (R² = 0.979 and MSE = 3.826 for active chlorine production and R² = 0.985 and MSE = 6.952 for energy consumption). Multi‐objective optimization for maximizing active chlorine production and minimizing energy consumption was carried out by a genetic algorithm using the best derived ANN models. The Pareto front obtained led to multiple non‐dominated optimal points, which result in insights regarding the optimal operating conditions for the process.

中文翻译：

---

人工神经网络和遗传算法：使用电解工艺生产活性氯的有效建模和优化方法

这项研究评估了基于人工神经网络和遗传算法的建模和优化方法在预测合成盐废水中活性氯的电解过程行为方面的有效性。基于以下输入，开发了多层感知器前馈神经网络用于有效氯的产生和能量消耗：电解时间，电流强度，盐酸浓度和氯离子浓度。为了在学习过程中诊断和防止过度拟合的问题，利用了学习曲线和正则化因子。经过训练的ANN模型能够成功预测过程中的有效氯产量和能耗（R ²活性氯的生产= 0.979，MSE = 3.826；能耗，R ² = 0.985，MSE = 6.952）。通过遗传算法，使用最佳派生的ANN模型，进行了多目标优化，以最大限度地提高活性氯的产生量并最小化能耗。获得的帕累托前沿导致多个非支配的最佳点，从而得出有关该过程的最佳操作条件的见解。