Ejimofor Marcel I, Ohale Paschal E, Aniagor Chukwunonso O, Onu Chijioke Elija, Menkiti Matthew C, Ezemagu Godfrey I, Chukwu Monday Morgan
Department of Chemical Engineering, Nnamdi Azikiwe University, P.M.B. 5025, Awka, Nigeria.
Department of Chemical Engineering, University of Agriculture, Umuagwo, Imo state, Nigeria.
Heliyon. 2024 Jul 10;10(14):e34229. doi: 10.1016/j.heliyon.2024.e34229. eCollection 2024 Jul 30.
This study investigated the application of artificial intelligence algorithms (AIA) in the coagulation treatment of paint wastewater anchored by novel seed extract (PVSE). Untreated wastewater discharge harms the ecosystem, and therefore harmful industrial effluent, such as paint wastewater, must be brought to safe discharge levels before being released into the environment. In addition to AIA, comprehensive characterization tests, coagulation kinetics, and process optimization were also executed. Characterization results revealed that total solid in the PWW was above allowable standard, justifying the need for effective particle decontamination. The XRD and FTIR characterization indicated that PVSE structure is amorphous with abundant amine groups. Results of analysis of variance (ANOVA) obtained from process modeling indicated that the coagulation-flocculation process was a nonlinear quadratic system (F-value = 45.51) which was mostly influenced by PVSE coagulant dosage (F-value = 222.48; standardized effect = 14.85). Artificial intelligence indicated that neural network training effectively captured the nonlinear nature of the system in ANN (RMSE = 0.00040194; R = 0.98497), and ANFIS (RMSE = 0.003961) algorithms. Regression coefficient obtained from process modeling highlighted the suitability of RSM (0.9662), ANN (0.9739), and ANFIS (0.9718) in forecasting the coagulation-flocculation process, while comparative statistical appraisal authenticated the superiority of ANN model over RSM and ANFIS models. The coagulation kinetics experiment, which used a coagulation kinetic model, revealed a constant flocculation constant (Kf-value) for all jar test batches and a strong association between the Menkonu coagulation-flocculation constant (Km) and Kf values. Best removal efficiency of 97.01 % was obtained using ANN coupled genetic algorithm optimization (ANN-GA) at PVSE dosage of 4 g/L, coagulation time of 29 min and temperature of 25.1C.
本研究调查了人工智能算法（AIA）在新型种子提取物（PVSE）锚定的油漆废水混凝处理中的应用。未经处理的废水排放会损害生态系统，因此，诸如油漆废水等有害工业废水在排放到环境之前必须达到安全排放标准。除了人工智能算法外，还进行了综合表征测试、混凝动力学和工艺优化。表征结果表明，油漆废水中的总固体含量高于允许标准，这证明了有效去除颗粒污染物的必要性。X射线衍射（XRD）和傅里叶变换红外光谱（FTIR）表征表明，PVSE结构为无定形，含有丰富的胺基。从工艺建模获得的方差分析（ANOVA）结果表明，混凝-絮凝过程是一个非线性二次系统（F值 = 45.51），主要受PVSE混凝剂用量影响（F值 = 222.48；标准化效应 = 14.85）。人工智能表明，神经网络训练有效地捕捉了人工神经网络（ANN）（均方根误差（RMSE）= 0.00040194；相关系数（R）= 0.98497）和自适应神经模糊推理系统（ANFIS）（RMSE = 0.003961）算法中系统的非线性特性。从工艺建模获得的回归系数突出了响应曲面法（RSM）（0.9662）、人工神经网络（0.9739）和自适应神经模糊推理系统（0.9718）在预测混凝-絮凝过程中的适用性，而比较统计评估证实了人工神经网络模型优于响应曲面法和自适应神经模糊推理系统模型。使用混凝动力学模型的混凝动力学实验表明，所有烧杯试验批次的絮凝常数（Kf值）恒定，且门科努混凝-絮凝常数（Km）与Kf值之间存在强关联。在PVSE用量为4 g/L、混凝时间为29分钟和温度为25.1℃的条件下，使用人工神经网络耦合遗传算法优化（ANN-GA）获得了97.01%的最佳去除效率。
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