Alghamdi Ahmed
Department of Chemical Engineering Technology, Yanbu Industrial College, Royal Commission Yanbu Colleges & Institutes, P.O. Box 30346, Yanbu Industrial City, 41912, Saudi Arabia.
Heliyon. 2023 Feb 17;9(3):e13814. doi: 10.1016/j.heliyon.2023.e13814. eCollection 2023 Mar.
Over the past years, Seawater Desalination (SWD) has been enhanced regularly. In this desalination process, numerous technologies are available. The Reverse Osmosis (RO) process, which requires effectual control strategies, is the most commercially-dominant technology. Therefore, for SWD, a novel Interpolation and Exponential Function-centered Deep Learning Neural Network (IEF-DLNN) and multi-objective-based optimizing control system has been proposed in this research methodology. Initially, the input data are gathered; then, to control the desalination process, an optimal control technique has been utilized by employing Probability-centric Dove Swarm Optimization-Proportional Integral Derivative (PDSO-PID). The attributes of permeate are extracted before entering the RO process; after that, by utilizing the IEF-DLNN, the trajectory is predicted. For optimal selection, the extracted attributes are deemed if the trajectory is present, or else to mitigate energy consumption along with cost, the RO Desalination (ROD) is performed. In an experimental evaluation, regarding certain performance metrics, the proposed model's performance is analogized with the prevailing methodologies. The outcomes demonstrated that the proposed system achieved better performance.
在过去几年中，海水淡化（SWD）一直在不断改进。在这种淡化过程中，有多种技术可供使用。反渗透（RO）工艺是商业上占主导地位的技术，它需要有效的控制策略。因此，在本研究方法中，针对海水淡化提出了一种以插值和指数函数为中心的深度学习神经网络（IEF-DLNN）和基于多目标的优化控制系统。首先，收集输入数据；然后，为了控制淡化过程，采用了以概率为中心的鸽群优化-比例积分微分（PDSO-PID）来运用最优控制技术。在进入反渗透工艺之前提取渗透物的属性；之后，利用IEF-DLNN预测轨迹。为了进行最优选择，如果存在轨迹，则考虑提取的属性，否则为了降低能耗和成本，进行反渗透淡化（ROD）。在实验评估中，针对某些性能指标，将所提出模型的性能与现有方法进行了比较。结果表明，所提出的系统取得了更好的性能。
