The use of integrated anaerobic-aerobic bioreactor (IAAB) to treat the Palm Oil Mill Effluent (POME) showed promising results, which successfully overcome the limitation of a large space that is needed in the conventional method. The understanding of synergism between anaerobic digestion and aerobic process is required to achieve maximum biogas production and COD removal. Hence, this work presents the use of artificial neural network (ANN) to predict the COD removal (%), purity of methane (%), and methane yield (LCH4/gCODremoved) of anaerobic digestion and COD removal (%), biochemical oxygen demand (BOD) removal (%), and total suspended solid (TSS) removal (%) of aerobic process in a pre-commercialized IAAB located at Negeri Sembilan, Malaysia. MATLAB R2019b was used to develop the two ANN models. Bayesian regularization backpropagation (BR) showed the best performance among the 12 training algorithms. The trained ANN models showed high accuracy (R2 > 0.997) and demonstrated good alignment with the industrial data obtained from the pre-commercialized IAAB over a 6-month period. The developed ANN model is subsequently used to create the optimal operating conditions which maximize the output parameters. The COD removal (%) was improved by 33.9% (from 68.7% to 92%), while the methane yield was improved by 13.4% (from 0.23 LCH4/gCODremoved to 0.26 LCH4/gCODremoved). Sensitivity analysis shows that COD inlet is the most influential input parameters that affect the methane yield, anaerobic COD, BOD and TSS removals, while for aerobic process, COD removal is most affected by mixed liquor suspended solids (MLSS). The trained ANN model can be utilized as a decision support system (DSS) for operators to predict the behavior of the IAAB system and solve the problems of instability and inconsistent biogas production in the anaerobic digestion process. This is of utmost importance for the successful commercialization of this IAAB technology. Additional input parameters such as the mixing time, reaction time, nutrients (ammonium nitrogen and total phosphorus) and concentration of microorganisms could be considered for the improvement of the ANN model.
Being the second-largest country in the production of palm oil, Malaysia has a massive amount of palm kernel expeller (PKE) leftover. For that purpose, black soldier fly larvae (BSFL) are thus employed in this study to valorize the PKE waste. More specifically, this work elucidated the effects of the pre-fermentation of PKE via different amounts of Rhizopus oligosporus to enhance PKE palatability for the feeding of BSFL. The results showed that fermentation successfully enriched the raw PKE and thus contributed to the better growth of BSFL. BSFL grew to be 34% heavier at the optimum inoculum volume of 0.5 mL/10 g dry weight of PKE as compared to the control. Meanwhile, excessive fungal inoculum induced competition between BSFL and R. oligosporus, resulting in a reduction in BSFL weight. Under optimum feeding conditions, BSFL also registered the highest lipid yield (24.7%) and protein yield (44.5%). The biodiesel derived from BSFL lipid had also shown good compliance with the European biodiesel standard EN 14214. The high saturated fatty acid methyl esters (FAMEs) content (C12:0, C14:0, C16:0) in derived biodiesel made it highly oxidatively stable. Lastly, the superior degradation rate of PKE executed by BSFL further underpinned the sustainable conversion process in attaining valuable larval bioproducts.
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