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Background: The booming palm oil industry is in line with the growing population worldwide and surge in demand. This leads to a massive generation of palm oil mill effluent (POME). POME is composed of sterilizer condensate (SC), separator sludge (SS), and hydro-cyclone wastewater (HCW). Comparatively, SS exhibits the highest organic content, resulting in various environmental impacts. However, past studies mainly focused on treating the final effluent. Therefore, this pioneering research investigated the optimization of pollutant removal in SS via different aspects of bioremediation, including experimental conditions, treatment efficiencies, mechanisms, and degradation pathways. Methods: A two-level factorial design was employed to optimize the removal of chemical oxygen demand (COD) and turbidity using Aspergillus niger. Bioremediation of SS was performed through submerged fermentation (SmF) under several independent variables, including temperature (20–40 °C), agitation speed (100–200 RPM), fermentation duration (72–240 h), and initial sample concentration (20–100%). The characteristics of the treated SS were then compared to that of raw sludge. Results: Optimal COD and turbidity removal were achieved at 37 °C 100 RPM, 156 h, and 100% sludge. The analysis of variance (ANOVA) revealed a significant effect of selective individual and interacting variables (p < 0.05). The highest COD and turbidity removal were 97.43% and 95.11%, respectively, with less than 5% error from the predicted values. Remarkably, the selected optimized conditions also reduced other polluting attributes, namely, biological oxygen demand (BOD), oil and grease (OG), color, and carbon content. In short, this study demonstrated the effectiveness of A. niger in treating SS through the application of a two-level factorial design. [ABSTRACT FROM AUTHOR]

Microwave pyrolysis of oil palm fibre (OPF) was conducted to study the effect of microwave power, temperature, and nitrogen (N2) flow rate on the production of hydrogen and biochar. The effect of microwave power ranging from 400 to 900 W, reaction temperatures ranging from 450°C to 700°C, and N2 flow rates ranging 200 to 1,200 cm3 min−1 were investigated. The microwave power, reaction temperature, and N2 flow rates are directly proportional to hydrogen and inversely proportional to biochar yield. The characteristics of the biochar were analysed using CHNOS, scanning electron microscopy, and Brunauer–Emmett–Teller results. Central composite design was used to optimize the number of experiment. The optimized experimental datasets were used as inputs for developing a model that can predict the weight of hydrogen and biochar from microwave pyrolysis of OPF. A multilayer feedforward neural network model was developed by comparing the performance of 11 different types of backpropagation training algorithms. The 11 training algorithms belong to six classes, which are (a) additive momentum, (b) self‐adaptive learning rate, (c) resilient backpropagation, (d) conjugate gradient, (e) quasi‐Newton, and (f) Bayesian regulation (BR). Best performing training algorithm was selected based on the lowest error values computed. The objective of this research is to identify the most suitable training algorithm for this process. Levenberg–Marquardt and BR exhibited very good performance. BR exhibited best performance with lowest error values in predicting weight of hydrogen and biochar. The testing data error values for hydrogen weight is 0.216 root mean square error (RMSE), 0.144 mean absolute error (MAE), and 0.020 mean absolute percentage error (MAPE), and biochar is 0.886 RMSE, 0.489 MAE, and 0.019 MAPE. The optimum number of hidden neuron for this algorithm is 19 with mean square error 0.23. The biochar exhibits porous structure suggesting possible land application. [ABSTRACT FROM AUTHOR]