Biohydrogen production from microalgae is a potential alternative energy source that is now intensively being researched. The complex natures of the biological processes involved have afflicted the accuracy of traditional modelling and optimization, besides being costly. Accordingly, machine learning algorithms have been employed to overcome setbacks, as these approaches have the capability to predict nonlinear interactions and handle multivariate data from microalgal biohydrogen studies. Thus, the review focuses on revealing the recent applications of machine learning techniques in microalgal biohydrogen production. The working principles of random forests, artificial neural networks, support vector machines, and regression algorithms are covered. The applications of these techniques are analyzed and compared for their effectiveness, advantages and disadvantages in the relationship studies, classification of results, and prediction of microalgal hydrogen production. These techniques have shown great performance despite limited data sets that are complex and nonlinear. However, the current techniques are still susceptible to overfitting, which could potentially reduce prediction performance. These could be potentially resolved or mitigated by comparing the methods, should the input data be limited.

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/gCOD_removed) 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/gCOD_removed to 0.26 LCH₄/gCOD_removed). 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.

Attached microalgal growth of Chlorella vulgaris on palm decanter cake (PDC) under irradiation with various visible monochromatic and polychromatic spectra to produce biodiesel was studied in this work. The results demonstrated that the white spectrum cultivation exhibited the highest microalgal density of 1.13 g/g along with 1.213 g/L day of microalgal productivity. Correspondingly, the biodiesel obtained was comprised mainly of C16 and C18 fatty acids, possessing a high cetane number and oxidation stability from the high saturated fatty acid content (70.38%), which was appealing in terms of most biodiesel production requirements. Nevertheless, the highest lipid content (14.341%) and lipid productivity (93.428 mg/L per day) were discovered with green spectrum cultivation. Blue and white spectra led to similar protein contents (34%) as well as carbohydrate contents (61%), corroborating PDC as a feasible carbon and nutrient source for growing microalgae. Lastly, the energy feasibilities of growing the attached microalgae under visible spectra were investigated, with the highest net energy ratio (NER) of 0.302 found for the yellow spectrum. This value outweighed that in many other works which have used suspended growth systems to produce microalgal fuel feedstock. The microalgal growth attached to PDC is deemed to be a suitable alternative cultivation mode for producing sustainable microalgal feedstock for the biofuel industry.