Start-up period is considered to be the most unstable and difficult stage in anaerobic process and usually takes a long time due to slow-degree adaptation of anaerobic microorganisms. In order to achieve a shorter start-up period, a novel modified anaerobic baffled reactor (MABR) has been developed in this study, where each modified baffle has its own characteristics (form/shape) to facilitate a treatment of recycled paper mill effluent (RPME). The results of physico-chemical characteristics showed that effluent from recycled paper mill consisted of 4328 mg L⁻¹ chemical oxygen demand (COD), 669 mg L⁻¹ biochemical oxygen demand and 501 mg L⁻¹ volatile fatty acid. It also consisted of variety of heavy metals such as zinc, magnesium, iron and nickel at concentrations of 1.39, 12.19, 2.39 and 0.72 mg L⁻¹, respectively. Performance of MABR during the start-up period showed that methane production reached 34.7% with COD removal of 85% at steady state. The result indicates that MABR was successfully operated during the start-up period in treating RPME within a period of less than 30 days.

Organic Loading Rate (OLR) effect on the performance of Modified Anaerobic Hybrid Baffled (MAHB) bioreactor treating recycled paper mill effluent (RPME) was investigated in this present study. The MAHB reactor is operated with a range of OLR of 0.14–4.00 gm COD/lit/day by varying the influent Chemical Oxygen Demand (COD) concentration of 1000, 2000, 3000 and 4000 mg/lit and Hydraulic Retention Time (HRT) of 1, 3, 5 and 7 days. From the experiment, it was found that the reactor performance decrease when OLRs increase. Highest specific methane yield is 8.47 lit CH4/day with OLR of 1.33 gm COD/lit/day. The highest methane content achieved is 91.63 % from total of 6.43 lit biogas/day at a range pH of 6.6–7.5. Effluent Volatile Fatty Acid (VFA) was below 35 mg/lit HOAC throughout the experiment while alkalinity where fluctuated at low OLR and slightly stable as the OLR increases. Higher OLRs of 2.0 to 3.0 gm COD/lit/day results in addition of inorganics in the reactor causing destabilization of the reactor and process failure, and thereby significantly affect the reactor performance in terms of organic removal. The OLR of 1.33 gm COD/lit/day was found to be optimum for MAHB reactor for the effective treatment of RPME both in terms of specific methane yield and COD removals.

Anaerobic wastewater treatment is receiving renewed interest because it offers a means to treat wastewater with lower energy investment. Because the microorganisms involved grow more slowly, such systems require clever design so that the microbes have sufficient time with the substrate to complete treatment without requiring enormous reactor volumes. The anaerobic baffled reactor has inherent advantages over single compartment reactors due to its circulation pattern that approaches a plug flow reactor. The physical configuration of the anaerobic baffled reactor enables significant modifications to be made; resulting in a reactor which is proficient of treating complex wastewaters which presently require only one unit, ultimately significant reducing capital costs. This paper also concerns about mechanism, kinetic and hydrodynamic studies of anaerobic digestion for future application of the anaerobic baffled reactor for wastewater treatment.

In this study, the response surface methodology (RSM) and artificial neural network (ANN) were employed to study the adsorption process of 2,4-dichlorophenoxyacetic acid (2,4-D) by using modified hydrogel, i.e., activated carbon poly(dimethylaminoethyl methacrylate) (AC/PDMAEMA hydrogel). The effect of pH, the initial concentration of 2,4-D and the activated carbon content on the removal of 2,4-D and adsorption capacity were investigated through the face-centered composite design (FCCD), optimal design and two-level factorial design. The response surface plot suggested that higher removal of 2,4-D and adsorption capacity could be achieved at the higher initial concentration of 2,4-D and lower pH and activated carbon content. The modeling and optimization for the adsorption process of 2,4-D were also carried out by different design methods of RSM and different training methods of ANN. It was found that among the three design methods of RSM, the optimal design has the highest accuracy for the prediction of 2,4-D removal and adsorption capacity (R² = 0.9958 and R² = 0.9998, respectively). The numerical optimization of the optimal design found that the maximum removal of 2,4-D and adsorption capacity of 65.01% and 65.29 mg/g, respectively, were obtained at a pH of 3, initial concentration of 2,4-D of 94.52 mg/L and 2.5 wt% of activated carbon. Apart from the optimization of process parameters, the neural network architecture was also optimized by trial and error with different numbers of hidden neurons in the layers to obtain the best performance of the response. The optimization of the neural network was performed with different training methods. It was found that among the three training methods of the ANN model, the Bayesian Regularization method had the highest R² and lowest mean square error (MSE) with the optimum network architecture of 3:9:2. The optimum condition obtained from RSM was also simulated with the optimized neural network architecture to validate the responses and adequacy of the RSM model.

The process kinetics of an anaerobic digestion process for treating recycled paper mill effluent (RPME) was investigated. A laboratory-scale modified anaerobic hybrid baffled reactor (MAHB) was operated at hydraulic retention times of 1, 3, 5, and 7 days, and the results were analyzed for the kinetic models. A kinetic study was conducted by examining the phase kinetics of the anaerobic digestion process, which were divided into three main stages: hydrolysis kinetics, acetogenesis kinetics, and methane production kinetics. The study demonstrated that hydrolysis was the rate-limiting step. The applied Monod and Contois kinetic models showed satisfactory prediction with ${\mu }_{max}$ values of 1.476 and 0.6796 L day⁻¹, respectively.