Efficiency of electrocoagulation method to treat chicken processing industry wastewater—modeling and optimization

Alternative sequential combinations of electrocoagulation with electrooxidation and peroxi-coagulation for effective treatment of adhesive production industry wastewater

Adhesive production industry wastewater can be characterized by high chemical oxygen demand (COD) sourced from high refractory organic contaminants and high total suspended solids (TSS) concentration. Biodegradability of the wastewater is low and wastewater quality is unstable. Various treatment processes have limited applicability in such characterized wastewater. In this study, the treatment performance of electrochemical processes was investigated. Because it is not possible to meet the discharge standards by application of only one process for high refractory organic content, sequential electrochemical processes were studied in this work. In the first step of the sequential process, electrocoagulation (EC) using Al electrodes by which better performance was achieved was applied. In the second step, electrooxidation (EO) and peroxi-coagulation (PC) processes were applied to the EC effluent. In EO, Ti/MMO was selected as the most effective anode whereas in PC, Fe was used as the anode, and graphite was used as the cathode. Box-Behnken Design was applied to optimize the operating conditions of EO and PC processes and to obtain mathematical model equations. In the EC process, 77% COD, 78.5% TSS, and 85% UV254 removal efficiency were obtained under the optimum conditions (pH 7.2, reaction time 35 min, and current density 0.5 mA/cm2). With the EO and PC processes applied to the effluent of EC, 68.5% COD, 77% TSS, and 83% UV254 removal and 77.5% COD, 87% TSS, and 86.5% UV254 removal were obtained, respectively. The specific energy consumption of EC-EO and EC-PC processes was 16.08 kWh/kg COD and 15.06 kWh/kg COD, respectively. Considering the treatment targets and process operating costs, it was concluded that both sequential electrochemical systems could be promising alternative systems for the treatment of adhesive production industry wastewater.

Enhancing municipal solid waste leachate treatment efficiency: AI-based prediction of electrocoagulation/flocculation recovery using iron electrodes

This study addresses a gap in municipal leachate (MUPL) treatment by introducing a pioneering application of artificial intelligence (AI) in the electrocoagulation/electroflocculation (EC/EF) process utilizing iron electrodes. The overarching aim is to demonstrate the efficacy of AI, particularly a multi-layer perceptron (MLP)-based feed-forward artificial neural network (ANN) incorporating the Levenberg-Marquardt (LMb) algorithm, in predicting and optimizing EC/EF outcomes for turbidity (TDY) removal. The research methodology involved experimentation and robust ANN data modeling. The significance of this work emerges from the successful integration of AI, showcasing its potential in advancing wastewater, demonstrated through a strong positive correlation (0.994) between the ANN model predictions and experimental outcomes. The study achieves a remarkable 99.4% TDY removal at an electrolysis time of 10 min and contributes valuable insights into the critical parameters influencing the EC/EF process. Results from the ANN modeling exhibit high predictive accuracy, supported by elevated R-squared values and minimal mean square error. Statistical analyses underscore the significance of key process parameters, highlighting the influential roles of current intensity and settling time. The study emphasized the favourable impact of maintaining an acidic pH range, as it reduced electrostatic repulsion between particles, facilitating pollutant agglomeration, and identified electrolysis time as a key factor in enhancing treatment efficiency, supported by a strong positive correlation between electrolysis time and TDY reduction. Energy cost savings were realized by not requiring temperature elevation. Achieving a 99.4% TDY removal translates to substantial reductions in other pollutants present in the MUPL, thereby elevating water quality and ensuring compliance.

Assessment of graphite electrode on the removal of anticancer drug cytarabine via indirect electrochemical oxidation process: Kinetics & pathway study

In this paper degradation of cytarabine drug has been studied through electrochemical oxidation process by using graphite electrode. The performance of graphite electrode on the degradation of cytarabine was evaluated by investigating the effects of key parameters: pH (3-9), current density (5-20 mA cm^-2) and initial pollutant concentration (5-50 mg L^-1) with 0.05 M NaCl as supporting electrolyte. Highest removal efficiency (98%) for 20 mg L^-1 of initial cytarabine solution was attained within 60 min electrolysis at 10 mA cm^-2. The increase in degradation rate of cytarabine was possibly because of the active chlorine species originated at anode during the electrolysis. Further, efficiency of the graphite electrodes was compared with a metal electrode (copper) and results showed that the cytarabine degradation was facilitated by the in-situ generated OH radicals. However, only 82% of cytarabine was removed after 60 min of reaction time at 15 mA cm^-2. The scum of Cu²⁺ ions deposited on the anode surface inhibit the mass transfer among the cytarabine molecules and generated hydroxyl radicals. The kinetic study also suggests faster reaction rate at graphite (0.12 min^-1) than copper (0.05 min^-1) electrode. The increase in electrolyte concentration enhanced the degradation rate and decreased the energy consumption from 3.66 to 0.66 kWh m^-3. Cytosine was identified as the major transformation product from the UV-Vis spectral analysis and LC-MS analysis. Further, total organic carbon analysis depicts that only 60% of the parent molecule was mineralized. Hence, graphite was found to be an efficient anode material as compared to copper for cytarabine degradation.

Decolourization of Reactive Dye from Aqueous Solution using Electrocoagulation: Kinetics and Isothermal Study

Reactive dyes are essential materials for the modern lifestyle due to rapid industrialization and urbanization, but they cause adverse effects on the environment. This research work aimed to decolourize the synthetic aqueous solution containing Reactive Black B (RBB) dye using electrocoagulation (EC) process with iron electrodes in batch reactor. The effect of operational parameters such as initial pH (3–9), the distance between electrodes (0.5–2 cm), current density (1.1–8.4 mA/cm2) and initial dye concentration (100–400 mg/L), was investigated in the presence of sodium chloride to maintain the conductivity of electrolytes. Under optimal value of process parameters, high decolourization (99.6%) was obtained at 25 min. The experimental data showed that pseudo-second order kinetics with a correlation coefficient (R 2 = 0.97) and Sips isotherm with a correlation coefficient (R 2 = 0.98) were found to be well fitted for kinetic and adsorption equilibrium models, respectively. The economic efficiency was also calculated on the basis of electrical energy consumption (EEC), specific electrical energy consumption (SEEC), and current efficiency, respectively. Moreover, characterization of EC generated sludge was also carried out by proximate analysis, IR spectra and XRD analysis. The results revealed that EC process using Fe electrode is quite efficient and clean process for decolourization of reactive dye from aqueous solution.

Optimizing electrocoagulation process using experimental design for COD removal from unsanitary landfill leachate

Leachate is the most difficult wastewater to be treated due to its complex content and high pollution release. For this reason, since it is not possible to be treated with a single process, a pre-treatment is needed. In the present study, a batch electrocoagulation reactor containing aluminum and iron electrodes was used to reduce chemical oxygen demand (COD) from landfill leachate (Tunceli, Turkey). Optimization of COD elimination was carried out with response surface methodology to describe the interaction effect of four main process independent parameters (current density, inter-electrode distance, pH and time of electrolysis). The optimum current density, inter-electrode distance, pH and time of electrolysis for maximum COD removal (43%) were found to be 19.42 mA/m², 0.96 cm, 7.23 and 67.64 min, respectively. The results shown that the electrocoagulation process can be used as a pre-treatment step for leachate.

Recovery of Biogas from Meat Industry Wastewater Using Continuously Stirred Tank Reactor (CSTR): Modeling and Optimization

The main objective of the present study is to investigate the biogas recovery process from meat industry wastewater using continuously stirred tank reactor (CSTR). Plackett–Burman design was used to screen the effective process variables among temperature, alkalinity dose, organic loading rate, agitation speed, nutrients, dilution factor and volatile fatty acid content. Box-Behnken design (BBD) was used to study the individual and interactive of process variables on the biogas recovery process. Optimum conditions for maximizing the biogas recovery was determined using derringer’s desired function methodology as follows; temperature of 45°C, organic loading rate of 8 m3/COD/day, C:N ratio of 90:10 and dilution factor of 15%. Under these optimal conditions, predicted biogas recovery and COD removal are found to be 0.08 m3/L/d and 94%, respectively. This result confirms the effectiveness of CSTR to treat meat industry wastewater in terms of biogas recovery.

Investigation on Fluidized Bed Bioreactor Treating Ice Cream Wastewater Using Response Surface Methodology and Artificial Neural Network

In this study, a comparative approach was developed between response surface methodology (RSM) and artificial neural network (ANN) in the predictive capabilities for the removal of chemical oxygen demand (COD) from ice cream industry wastewater using fluidized bed bioreactor. The effects of process variables such as pH, temperature, flow rate and agitation speed investigated using a four-factor three-level Box–Behnken experimental design (BBD). Same design was utilized to train a feed-forward multilayered perceptron (MLP) ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of statistical parameters including coefficient of determination (R2). The results showed that properly trained ANN model is more accurate in prediction as compared to RSM model. Under the optimum conditions (pH of 7, temperature of 40°C, flow rate of 20 ml/min and agitation speed of 175 rpm), 91% of COD was removed.