Comparative Simulation of Advanced Oxidation Process and Electrocoagulation for Wastewater Treatment: A Two-Dimensional Diffusion–Reaction Study Surpinder Singh1, Abhilash Thakur2 1Research Scholar, Department of Civil Engineering, CT University, Ludhiana 2Assistant Professor, Department of Civil Engineering, CT University, Ludhiana Article History: Received: 15-10-2024 Revised: 26-11-2024 Accepted: 25-12-2024 Abstract: The growing challenge of wastewater pollution from industrial and domestic sources necessitates the adoption of advanced treatment technologies capable of removing recalcitrant pollutants. This study presents a comparative simulation of two promising processes—Advanced Oxidation Process (AOP) and Electrocoagulation (EC)—in a batch reactor configuration. The AOP simulation models pollutant degradation using UV- induced photocatalytic reactions gov-erned by Langmuir–Hinshelwood kinetics, while the EC model captures electric field gener-ation, Faradaic coagulant production, and its interaction with pollutants. Both systems are implemented in a two-dimensional domain using finite difference methods, simulating pollu-tant and reactant transport via diffusion- reaction equations under no-mixing conditions. Spatial concentration profiles, COD removal trends, and energy usage are evaluated to understand sys-tem performance and limitations. Results show that AOP achieves 33% pollutant removal in 8 hours, primarily near the UV-illuminated surface, consuming approximately 0.4 kWh. In con-trast, EC achieves 23% removal, with degradation localized near the anode, consuming 0.102 kWh. Both processes are shown to be highly diffusion-limited in the absence of stirring, with steep concentration gradients restricting treatment to localized zones. These findings under-score the need for enhanced mass transport—either via mixing or flow—to achieve uniform performance. The study demonstrates the utility of simulation in evaluating spatial and ener-getic behavior of advanced treatment systems and provides a foundation for optimizing design parameters, operational strategies, and energy efficiency in future implementations. The mod-ular simulation framework can be extended to include convection, multi-species interactions, or real reactor geometries for broader application in wastewater engineering. Keywords: Advanced Oxidation Process (AOP), Electrocoagulation (EC), Wastewater Treat-ment, Photocatalysis, Langmuir–Hinshelwood Kinetics, Faraday’s Law, Energy Efficiency. 1. Introduction The growing environmental concerns associated with industrial effluents have intensified re-search into effective and sustainable wastewater treatment technologies. In particular, wood-based industries generate highly polluted wastewater characterized by elevated concentrations of organic compounds such as lignin, phenols, and suspended solids, along with heavy metals and colorants. These pollutants are recalcitrant and pose significant threats to aquatic ecosys-tems and public health [1]. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 957 Advanced Oxidation Processes (AOPs) have emerged as potent techniques capable of degrad- ing persistent organic pollutants by generating highly reactive hydroxyl radicals (•OH) [2, 3, 4]. Among them, UV/TiO2 photocatalysis and the Fenton process have shown high potential for treating industrial wastewater [5, 6, 7]. Recent studies emphasize the integration of multiple AOPs to enhance degradation efficiency, particularly under variable conditions like pH and con- taminant load [8, 9, 10]. For instance, the photoelectro-Fenton (PEF) technique demonstrates synergistic effects by combining photo-irradiation with electrochemical reactions [11, 12]. Electrocoagulation (EC), another promising technique, operates by applying electric current through sacrificial electrodes (usually iron or aluminum), producing coagulants in situ [13, 14]. These coagulants destabilize and aggregate pollutants, enabling their removal via sedimentation or flotation. EC has shown efficacy in treating diverse contaminants, including heavy metals, suspended solids, and organic matter [15, 16]. It is energy-efficient and generates less sludge than conventional coagulation methods, making it attractive for industrial-scale applications [17, 18]. However, the combined use of AOPs and EC—particularly for the wood industry—remains un- derexplored. Few studies have addressed their integration and optimization under site-specific conditions such as the high COD and varying pH levels typical of wood-based effluents [19, 20]. The use of multiphysics simulation tools like COMSOL offers a powerful platform for model- ing and optimizing these processes [21, 22]. By simulating flow dynamics, mass transfer, and chemical reactions, COMSOL enables the prediction of treatment efficiency and helps mini- mize energy consumption and operational costs [23, 24]. Moreover, real-time monitoring and adaptive control via IoT frameworks are emerging as in- novative strategies to dynamically regulate treatment processes [?]. Integrating these tools can facilitate responsive, cost-effective, and environmentally sound treatment solutions tailored to the wood sector’s needs [26, 27, 28]. This study aims to simulate and optimize integrated AOP and EC systems using COMSOL and MATLAB for effective treatment of wood-based industrial wastewater. It contributes to filling knowledge gaps in pollutant-specific parameter tuning, hybrid process modeling, and by-product management, aligning with the objectives of cost-efficiency, scalability, and envi- ronmental compliance [29, 30]. Objectives: Lacinia at quis risus sed vulputate odio ut enim... Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 958 2. Methodology This chapter outlines the computational modeling approaches used to simulate two advanced wastewater treatment technologies: the Advanced Oxidation Process (AOP) and Electrocoag- ulation (EC). Both processes are modeled in a two-dimensional domain representing a batch reactor configuration. The AOP simulation focuses on pollutant degradation driven by UV- activated photocatalytic reactions, while the EC simulation captures the generation and trans- port of coagulant ions through an applied electric field and their interaction with pollutants. The following subsections detail the physical assumptions, governing equations, boundary condi- tions, and numerical implementation strategies for each method. 2.1. Advanced Oxidation Process (AOP) Simulation This section presents the development and implementation of a two-dimensional numerical simulation of a photocatalytic Advanced Oxidation Process (AOP). The model captures the degradation of pollutants in water through a diffusion-reaction mechanism, governed by Lang- muir–Hinshelwood (L–H) surface kinetics. The simulation domain mimics a flat photoreactor irradiated with ultraviolet (UV) light from the top boundary. The aim is to evaluate the pollu- tant concentration distribution over time and space, determine the Chemical Oxygen Demand (COD) removal efficiency, and estimate the energy required for treatment. Governing Equations Let C(x, y, t) represent the pollutant concentration in mg/L at spatial location (x, y) ∈ Ω ⊂ R2 and time t ∈ [0, T ]. The governing partial differential equation for pollutant transport is defined as: ∂C ∂t = D ( ∂2C ∂x2 + ∂2C ∂y2 ) −R(C, y), (1) where D is the diffusion coefficient (m2/s), and R(C, y) is the non-linear reaction term gov- erned by depth-dependent UV intensity: R(C, y) = I0e −αy · kLH ·K · C 1 +K · C . (2) Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 959 Here, kLH is the intrinsic reaction rate constant (s−1), K is the adsorption equilibrium constant (L/mg), α is the UV attenuation coefficient (m−1), and I0 is the surface UV intensity (normal- ized to 1.0). Initial and Boundary Conditions At time t = 0, the initial pollutant concentration is uniformly distributed: C(x, y, 0) = C0 = 100 mg/L. (3) The reactor boundaries are treated as impermeable (zero-flux), yielding homogeneous Neu- mann conditions: ∂C ∂n ∣∣∣∣ ∂Ω = 0. (4) Numerical Discretization The domain Ω = [0, Lx] × [0, Ly] is discretized into a uniform grid of size Nx × Ny with spacing ∆x and ∆y. Let Cn i,j denote the concentration at spatial node (i, j) and timestep n. The Laplacian is approximated using second-order central differences: ∇2C ≈ Cn i+1,j − 2Cn i,j + Cn i−1,j ∆x2 + Cn i,j+1 − 2Cn i,j + Cn i,j−1 ∆y2 . (5) The explicit forward Euler method is used for time integration: Cn+1 i,j = Cn i,j +∆t [ D∇2Cn i,j −R(Cn i,j, yj) ] , (6) with the reaction term evaluated as: R(Cn i,j, yj) = e−αyj · kLH ·K · Cn i,j 1 +K · Cn i,j . (7) To ensure stability, the timestep ∆t is chosen based on the Courant–Friedrichs–Lewy (CFL) condition: ∆t < 1 4D ( 1 ∆x2 + 1 ∆y2 )−1 . (8) Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 960 Performance Metrics The average concentration over the domain at time tn is computed as: C̄(tn) = 1 NxNy Nx−1∑ i=0 Ny−1∑ j=0 Cn i,j. (9) The corresponding COD removal efficiency is: η(tn) = ( 1− C̄(tn) C0 ) × 100%. (10) Assuming a 50 W UV lamp, the total energy consumed over time T is: EkWh = P · T 3.6× 106 , (11) where P = 50 W and T = 28800 seconds (8 hours). Simulation Setup and Output The simulation is executed on a 50 × 50 grid over a 0.1 × 0.1 m2 domain using ∆t = 10 seconds. Parameters used are: D = 1 × 10−9 m2/s, kLH = 1 × 10−3 s−1, K = 0.1 L/mg, α = 10 m−1. 2.2. Electrocoagulation (EC) Simulation This section describes the mathematical modeling and numerical simulation of an Electrocoag- ulation (EC) process for wastewater treatment. In EC, an external DC voltage is applied across a pair of electrodes submerged in the electrolyte, leading to the in situ generation of coagulant metal ions (e.g., Fe2+ or Al3+) at the sacrificial anode. These ions destabilize and aggregate pollutants (e.g., suspended solids, COD, dyes), which can then be removed from the treated solution. Domain and Electrochemical Configuration The simulation considers a two-dimensional cross-section of a batch electrocoagulation reactor. The domain is a rectangular region Ω = [0, Lx] × [0, Ly] representing a layer of fluid between two vertical planar electrodes. The left boundary x = 0 corresponds to the anode, and the right boundary x = Lx to the cathode. The vertical extent of the domain Ly matches the electrode height. Top and bottom boundaries are non-conductive and insulated. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 961 A constant voltage V0 is applied across the electrodes, resulting in a steady-state potential field Φ(x, y) governed by Laplace’s equation: ∇2Φ = ∂2Φ ∂x2 + ∂2Φ ∂y2 = 0, (x, y) ∈ Ω, (12) with Dirichlet boundary conditions: Φ(0, y) = V0, Φ(Lx, y) = 0, (13) and homogeneous Neumann boundary conditions on the horizontal edges: ∂Φ ∂y ∣∣∣∣ y=0 = 0, ∂Φ ∂y ∣∣∣∣ y=Ly = 0. (14) For a symmetric parallel-plate configuration, the potential field reduces to a linear profile: Φ(x) = V0 ( 1− x Lx ) . (15) The electric field magnitude is |∇Φ| = V0 Lx , and the resulting uniform current density in the electrolyte is given by Ohm’s law: J = σ|∇Φ| = σ V0 Lx , (A/m2), (16) where σ is the electrolyte conductivity (S/m). Faradaic Coagulant Generation At the anode, the applied current drives oxidation of the electrode material (e.g., Fe), generating soluble coagulant ions via the half-reaction: Fe → Fe2+ + 2e−. The generation rate of Fe2+ is governed by Faraday’s law: FluxFe2+ = J zF (mol/m2 · s), (17) where z = 2 is the ionic charge number, and F = 96485 C/mol is Faraday’s constant. Con- verting to concentration: Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 962 SM(x, y) =  J ·Mw zF ·Vcell , x = 0 (anode) 0, elsewhere (18) where Mw is the molar mass of Fe (55.85 g/mol), and Vcell is the control volume into which the flux is added. The concentration of Fe2+, denoted as M(x, y, t) (mg/L), evolves according to a diffusion equation with source term: ∂M ∂t = DM∇2M + SM(x, y), (19) where DM is the diffusion coefficient of Fe2+ (typically 1×10−9 m2/s). All domain boundaries except the anode are modeled as impermeable: ∂M ∂n ∣∣∣∣ ∂Ω\{x=0} = 0. (20) Pollutant Transport and Coagulation Kinetics A representative pollutant species is denoted by its concentration P (x, y, t) in mg/L. The pol- lutant is removed via a second-order irreversible reaction with the coagulant: ∂P ∂t = DP∇2P − kreact ·M · P, (21) where DP is the pollutant diffusion coefficient (m2/s), and kreact is the reaction rate constant (L/mg·s). We assume a 1:1 stoichiometry between pollutant and coagulant consumption. Ini- tially, the pollutant concentration is uniform: P (x, y, 0) = P0 = 100 mg/L, M(x, y, 0) = 0. (22) All domain boundaries are assumed to be no-flux for the pollutant: ∂P ∂n ∣∣∣∣ ∂Ω = 0. (23) Simulation Parameters and Metrics The domain is configured as a square with Lx = Ly = 0.1 m, discretized into a 50 × 50 grid. The applied voltage is V0 = 5 V, and the electrolyte conductivity is σ = 0.5 S/m, yielding a current density J = 25 A/m2. The simulation time horizon is 8 hours, with outputs recorded at Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 963 fixed intervals. The average pollutant concentration at time tn is: P̄ (tn) = 1 |Ω| ∫ Ω P (x, y, tn) dx dy, (24) and the COD removal efficiency is: η(tn) = ( 1− P̄ (tn) P0 ) × 100%. (25) Total energy consumed is estimated by: EkWh = V0 · I · T 3.6× 106 , (26) where I = J ·A is total current (A) over electrode area A, and T is the treatment time (s). The total mass of Fe dissolved is derived from Faraday’s law: mFe = I · T ·Mw zF (g). (27) This simulation framework quantifies spatial coagulant dispersion, pollutant removal kinetics, and system energy efficiency. It enables parametric analysis of electrode voltage, pollutant reactivity, and electrochemical dosing, and serves as a base for optimization of batch EC pro- cesses under diffusion-limited conditions. 3. Results and Discussion This section presents and analyzes the simulation results obtained from two treatment pro- cesses: Advanced Oxidation Process (AOP) and Electrocoagulation (EC). Each simulation is evaluated based on spatial distribution of reactants and pollutants, temporal degradation kinet- ics, and energy performance over an 8-hour batch operation. Advanced Oxidation Process (AOP) Results Figure 1 shows the simulation outputs for the photocatalytic AOP model. The left subplot displays the final pollutant concentration field in the reactor after 8 hours of UV irradiation. The top boundary of the domain (y = 0) receives maximum light intensity, resulting in the highest rate of pollutant degradation. A clear vertical gradient is observed: the COD concentration decreases from 100 mg/L to below 55 mg/L near the illuminated surface, while the bottom Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 964 region retains significantly more pollutant due to limited light penetration and slow upward diffusion. The right subplot shows the time evolution of the spatially averaged COD. Initially, the re- moval is fast due to high surface concentration and strong reaction rates governed by Lang- muir–Hinshelwood kinetics. Over time, the removal slows down as concentration gradients develop and the process becomes diffusion-limited. After 8 hours, approximately 33% of the COD is removed, reducing the average concentration to 67 mg/L. Figure 1: AOP simulation results: (Left) Final pollutant concentration (mg/L) in the 2D pho- toreactor domain. Top surface (UV-illuminated) shows significant degradation. (Right) Time- series of average COD concentration, indicating 33% removal over 8 hours. The energy consumed by the UV lamp during this period is estimated to be 0.4 kWh. For a 10 L volume, this translates to an energy density of 40 kWh/m3, which is typical for photocatalytic batch reactors. The results illustrate that AOP is most effective near the surface, and additional enhancement (e.g., mixing or improved catalyst distribution) would be required for uniform degradation throughout the volume. Electrocoagulation (EC) Results The EC simulation results are presented in Figures 2 and 3. Figure 2 (left) illustrates the steady- state electric potential across the domain, confirming a linear voltage drop from 5 V at the anode to 0 V at the cathode. The vertical iso-potential lines verify the uniformity of the electric field, which yields a constant current density of approximately 25.5 A/m2. The right subplot of Figure 2 shows the spatial distribution of Fe2+ ions after 8 hours. The coagulant concentration is extremely high at the anode boundary (x = 0) and falls sharply within a few millimeters. The color scale is logarithmic, highlighting that the diffusion of coagulant ions is severely limited in the absence of stirring or convective flow. Figure 3 (left) presents the final pollutant distribution (COD) after 8 hours. The degradation is Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 965 Figure 2: EC simulation results: (Left) Electric potential field (V) between electrodes, showing linear voltage drop from 5 V to 0 V. (Right) Coagulant (Fe2+) concentration after 8 hours. Coagulant is highly concentrated near the anode and diffuses minimally into the bulk. confined to a narrow region near the anode where sufficient coagulant is present. The majority of the reactor volume remains untreated at 100 mg/L due to lack of coagulant reach. The right subplot shows the time evolution of average pollutant concentration. The COD drops from 100 mg/L to approximately 77 mg/L—achieving a 23% removal. The decline rate slows over time, reflecting transition from coagulant-rich fast reaction to diffusion-limited kinetics in the bulk. This behavior demonstrates that EC without mixing leads to localized treatment, with diminishing returns at longer times. Figure 3: EC simulation results: (Left) Pollutant (COD) concentration field after 8 hours. Degradation is localized near the anode. (Right) Time-series of average COD concentration showing 23% removal. The curve flattens as the process becomes diffusion-limited. The total electrical energy consumed during the 8-hour treatment is approximately 0.102 kWh. For a 10 L volume, this equates to 10.2 kWh/m3, which is within the practical range for EC batch treatment but suggests suboptimal efficiency due to lack of coagulant utilization in the full domain. Approximately 21 g of Fe is dissolved at the anode, yet much of it remains Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 966 concentrated near the electrode, unused. This emphasizes the need for controlled dosing or hydrodynamic mixing to improve efficiency. Comparative Summary Both AOP and EC processes achieve partial pollutant removal under diffusion-limited condi- tions. AOP shows effective degradation at the UV-exposed surface, while EC exhibits removal near the anode. In both cases, lack of mixing results in spatially heterogeneous performance. AOP reaches 33% COD removal with 0.4 kWh energy use; EC reaches 23% removal with 0.102 kWh. The simulation results suggest that: • AOP is suitable for surface-reactive pollutants but limited in depth unless mixing or flow is introduced. • EC generates coagulants effectively, but their transport governs pollutant reach, making stirring or recirculation essential for scale-up. • Both simulations provide valuable tools for testing operational strategies such as reaction time, voltage optimization, and catalyst dosage. These findings underscore the critical role of mass transport in batch reactor performance and provide a reproducible, physics-based simulation platform for advanced wastewater treatment research. 4. Conclusion This work presents a detailed comparative simulation of Advanced Oxidation Process (AOP) and Electrocoagulation (EC) for batch-mode wastewater treatment using two-dimensional fi- nite difference models. The AOP simulation effectively captures UV-induced photocatalytic degradation with Langmuir–Hinshelwood kinetics, demonstrating high pollutant removal at the surface-exposed zone. The EC model simulates electrode-driven coagulant production via Faraday’s law and its diffusion-limited interaction with pollutants. Results from both mod- els reveal strong spatial dependence in pollutant removal due to the absence of mixing: AOP shows degradation confined to the UV-illuminated surface, while EC displays localized re- moval near the anode. Quantitatively, AOP achieved 33% removal with 0.4 kWh energy in- put, and EC achieved 23% removal using 0.102 kWh. These findings highlight that transport mechanisms, not chemical kinetics, are the primary bottleneck under stagnant conditions. The simulation framework enables visualization of concentration gradients, kinetic transition zones, and time-dependent performance, offering insights into process limitations and opportunities Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 967 for optimization. For both technologies, introducing stirring or flow would drastically improve pollutant-reactant interaction and reactor utilization. The methodology provides a scalable and modular tool for process analysis, adaptable to various geometries, operating parameters, and contaminant profiles. Future work can incorporate convective effects, dynamic flow fields, and real wastewater data to extend these findings into practical design recommendations. Ul-timately, this modeling approach supports the rational engineering of high-efficiency, energy-conscious advanced treatment systems for sustainable water resource management. References [1] M. Marques, S. Laohaprapanon, and W. Hogland, “Characterization and Treatment of Wood-Based Panel Industry Wastewater,” Int. J. Environ. Res. Public Health, vol. 17, no. 7, p. 2369, 2020. [2] X. Li, G. Chen, and H. Zhang, “Degradation of organic pollutants in water by photocat- alytic processes,” J. Environ. Manage., vol. 156, pp. 232–240, 2015. [3] M. Hassaan and A. Nemr, “Advanced oxidation processes for textile wastewater treat- ment,” Int. J. Photoenergy, 2017. [4] S. Agrawal et al., “Wastewater Treatment Containing Organic Pollutants Using AOPs,” IJSRST, 2023. [5] S. Wang, H. Liu, and J. Sun, “Photocatalytic degradation of organic pollutants in wastew- ater using TiO2,” J. Photochem. Photobiol. A, vol. 317, pp. 82–91, 2016. [6] M. Petrini, M. Kaur, and R. Bhardwaj, “Review on AOPs for wood-based wastewater,” J. Clean. Prod., vol. 194, pp. 371–379, 2018. [7] C. V. Rekhate and J. K. Srivastava, “Ozone-based AOPs for wastewater treatment,” Chem. Eng. J. Adv., vol. 3, p. 100031, 2020. [8] G. Boczkaj and A. Fernandes, “AOPs at basic pH conditions,” Chem. Eng. J., vol. 320, pp. 608–633, 2017. [9] H. Hansson et al., “Advanced Oxidation Treatment of Recalcitrant Wastewater,” Water Air Soil Pollut., vol. 226, no. 7, p. 229, 2015. [10] H. Xia et al., “Microwave-assisted AOPs,” Chemosphere, vol. 287, p. 131981, 2021. [11] E. Brillas, “Review on the photoelectro-Fenton process,” Chemosphere, vol. 250, p. 126198, 2020. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 968 [12] R. Patel, A. Kumar, and P. Mehta, “Hybrid AOP systems for industrial wastewater,” Env- iron. Technol. Innov., vol. 33, p. 101839, 2024. [13] M. Mollah et al., “Electrocoagulation for wastewater treatment,” Environ. Sci. Technol., vol. 44, no. 5, pp. 2012–2020, 2010. [14] I. Heidmann and W. Calmano, “Electrocoagulation for the treatment of wastewater,” Wa- ter Res., vol. 44, no. 6, pp. 1909–1917, 2010. [15] P. Kumar et al., “Enhancement of electrocoagulation process,” J. Environ. Chem. Eng., vol. 6, no. 2, pp. 3456–3465, 2018. [16] S. Arora, H. Singh, and R. Chauhan, “Integrated AOPs and EC for COD Reduction,” Water Res., vol. 234, p. 119123, 2024. [17] D. Sharma and K. Verma, “Developments in Electrode Materials for EC,” J. Environ. Chem. Eng., vol. 12, no. 1, p. 110983, 2024. [18] D. Ogundele et al., “Electrochemical Processes for Wastewater Treatment,” Clean. Eng. Technol., vol. 5, p. 100384, 2023. [19] D. Ma et al., “Review of AOPs in organic wastewater treatment,” Chemosphere, vol. 275, p. 130104, 2021. [20] N. Gupta et al., “Environmental Impact of AOP By-products,” Chemosphere, vol. 350, p. 138921, 2024. [21] H. Chen, J. Wang, and Y. Liu, “COMSOL Multiphysics for AOPs and EC,” Water Res., vol. 149, pp. 172–182, 2019. [22] P. Singh et al., “Real-Time Monitoring in AOPs,” Environ. Monit. Assess., vol. 196, no. 4, p. 417, 2024. [23] J. Yu et al., “Ti4O7 REM for Electrochemical Oxidation,” Sustainability, vol. 15, no. 21, p. 15488, 2023. [24] J. Wang et al., “Pulse Electrochemical Oxidation for Dye Wastewater,” Sep. Purif. Tech- nol., vol. 230, p. 115851, 2020. [25] P. Singh et al., “IoT Adaptive Control in AOP Systems,” Environ. Monit. Assess., 2024. [26] S. Ganiyu et al., “Reactive Species in EAOPs,” Curr. Opin. Electrochem., vol. 28, p. 100678, 2021. [27] L. Gil et al., “Fe2+ Oxidation Modeling,” Environ. Eng. Sci., vol. 39, no. 3, pp. 179–188, 2022. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 969 [28] R. Chandra et al., “Electro-oxidation for Bacterial Decontamination,” J. Environ. Chem. Eng., vol. 11, no. 2, p. 107578, 2023. [29] M. Ibrahim and J. Salman, “Successive EC and EO for Refinery Wastewater,” Process Saf. Environ. Prot., vol. 158, pp. 410–420, 2022. [30] R. Ken and A. Sinha, “Electrooxidation of Coke-Oven Wastewater,” J. Water Process Eng., vol. 40, p. 101982, 2021. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 8s (2024) https://internationalpubls.com 970 Introduction Methodology Advanced Oxidation Process (AOP) Simulation Electrocoagulation (EC) Simulation Results and Discussion Conclusion