Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)₁₂ but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.

Anaerobic co-digestion (ACD), where two or more substrates are digested simultaneously, is able to prevent the problems associated with mono-digestion. The aim of this study is to develop a simulation model of ACD of food waste (FW) with sewage sludge (SS) for biogas production coupled with pre-treatment, sludge handling and biogas upgrading using SuperPro Designer v9.0. The Design Expert v13 is employed to perform optimization and evaluate the effect of hydraulic retention time (HRT), sludge recycle ratio, water to feed ratio (kg/kg) and SS to FW ratio (kg/kg) on the methane flow, chemical oxygen demand (COD) and volatile solids (VS). The results show that the methane yield of 0.29 L CH4/g COD removed, COD removal efficiency of 81.5% and VS removal efficiency of 69.2% are obtained with a HRT of 38.8 days, water to feed ratio (kg/kg) of 0.048, sludge recycle ratio of 0.438 and SS to FW ratio (kg/kg) of 0.044. Economic analysis has shown this study is feasible with a payback time of 6.2 years, net present value (NPV) of $5,283,000 and internal return rate (IRR) of 10.2%. This indicates that the ACD of FW and SS is economically feasible in a larger scale.

The aim of this study was to spur the lipid accumulation by larvae of Hermetia illucens or black soldier fly (BSFL) via feeding with yeast fermented medium. The Saccharomyces cerevisiae, a single cell yeast, was introduced at different concentrations (0.02, 0.1, 0.5, 1.0, 2.5 wt %) to execute an in-situ fermentation on coconut endosperm waste. The rearing of BSFL was started simultaneously and the rearing was stopped once the BSFL reached the fifth instar. With the increasing of yeast concentration, the rearing duration of BSFL was shortened from 15.5 to 13.5 days. Moreover, it was found that at 0.5 to 1.0 wt % yeast concentration, the lipid yield and lipid productivity of BSFL were statistically enhanced to their highest peaks, namely, at 49.4% and 0.53 g/day, respectively. With regard to biodiesel composition, BSFL-derived biodiesel contained mainly C12:0, C14:0, C16:0 and C18:1. The higher amount of saturated fatty acids could strengthen the oxidative stability biodiesel produced as compared with non-edible oils or microalgal lipid. At last, the addition of yeast was also found to improve the waste reduction index of coconut endosperm waste (CEW) from 0.31 to 0.40 g/day, heralding the capability of BSFL to valorize organic waste via bioconversion into its biomass to serve as a feedstock for biodiesel production.

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.

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

Coconut endosperm waste (CEW) was treated by Rhizopus oligosporus via in situ and ex situ fermentations together with bioconversion into valuable black soldier fly larval biomass. The ex situ fermentation could overall enrich the nutritional compositions of CEW by hydrolyzing its complex organic polymers and exuding assimilable nutrients to enhance the black soldier fly larvae (BSFL) growth. Nevertheless, the larval gut bacteria were competing with Rhizopus oligosporus in in situ fermentation, derailing the hydrolysis processes and larval growth. Accordingly, the highest growth rates achieved were around 0.095 g/day, as opposed to only 0.065 g/day whilst using 0.5 wt% of Rhizopus oligosporus to perform ex situ and in situ fermentations, respectively. These were also underpinned by the greater amount of total CEW consumed when employing ex situ fermentation, with comparable metabolic costs to feeding on in situ-fermented CEW. The mature BSFL were subsequently harvested and the amounts of protein and lipid produced were assessed in terms of their feasibility for biodiesel production. While the statistical analyses showed that the larval protein yields derived from both fermentation modes were insignificant, the BSFL could attain higher lipid and protein productivities upon feeding with ex situ- rather than in situ-fermented CEW mediums. Better yet, the larval biodiesel quality measured in terms of the fatty acid methyl ester composition were not varied significantly by Rhizopus oligosporus through the fermentation process. Thereby, the presence of 1.0 wt% Rhizopus oligosporus was considered optimum to perform ex situ fermentation, giving rise to the acceptable growth of BSFL loaded with the highest lipid yield and productivity for producing biodiesel and protein simultaneously.