Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process

Abstract

Predicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artificial neural network (ANN) for predicting biochemical oxygen demand (BOD) removal under different adsorption conditions. Nanozero-valent iron encapsulated into cellulose acetate (CA/nZVI) was synthesized, characterized by XRD, SEM, and EDS, and used as an efficient adsorbent for BOD reduction. Results indicated that the medium pH and adsorption time should be adjusted around 7 and 30 min, respectively, to maintain the highest BOD removal efficiency of 96.4% at initial BOD concentration C o = 100 mg/L, mixing rate = 200 rpm, and adsorbent dosage of 3 g/L. An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal ( R 2 : 0.972, Adj- R 2 : 0.971, RMSE: 1.449, and SSE: 56.680). Based on the ANN sensitivity analysis, the relative importance of the adsorption factors could be arranged as pH > adsorbent dosage > time ≈ stirring speed > C o . A quadratic regression model was developed to visualize the impacts of adsorption factors on the BOD removal efficiency, optimizing pH at 7.3 and time at 46.2 min. The accuracy of the quadratic regression and ANN models in predicting BOD removal was approximately comparable. Hence, these computational-based methods could further maximize the performance of CA/nZVI material for removing BOD from wastewater under different adsorption conditions. The applicability of these modeling techniques would guide the stakeholders and industrial sector to overcome the nonlinearity and complexity issues related to the adsorption process.

Optimization of photocatalytic degradation of biochemical oxygen demand from textile industry effluent using copper oxide nanoparticles by response surface methodology

“…This fit represented the proper interaction of nanoparticles for BOD removal under the influence of physical adsorption and chemical adsorption pathways. The results of the other adsorption study were in agreement with our results 56 …”

Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

“…The histogram of the given input and output variables (dataset) is shown in Figure 9. Because the units and ranges of the parameters were different, the dataset was normalized between −1 and 1 using the standard normalization function (see Equation (11)). This step is essential to prevent numerical overflows that might occur from very large or very small weights.…”

“…Evaporation, adsorption, precipitation, membrane separation, electrocoagulation, and ion exchange are the common methods employed to eliminate heavy metals, including Cu ions, from industrial wastewater sources [9,10]. The adsorptionbased wastewater treatment method has encouraged researchers' motivation because of its simplicity of implementation and design, easy recovery of heavy metals, relatively low operation cost, and the feasibility of reusing the spent sorbent [11][12][13]. Adsorption is also characterized by ease of operation, and generation of low amounts of residue (e.g., sludge) [13,14].…”

Fourth Industrial Revolution of Wastewater Treatment with Adsorption

“…Mostafa et al [16] used the adsorption process to remove organic pollutants (expressed by biochemical oxygen demand; BOD) from wastewater. For this purpose, zero-valent iron nanoparticles were prepared and encapsulated into cellulose acetate, giving CA/nZVI adsorbent.…”