The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3-N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3-N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3-N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3-N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3-N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3-N content.
Metal-organic framework (MOF)--based composites have received significant attention in a variety of applications, including pollutant adsorption processes. The current investigation was designed to model, forecast, and optimize heavy metal (Cu2+) removal from wastewater using a MOF nanocomposite. This work has been modeled by response surface methodology (RSM) and artificial neural network (ANN) algorithms. In addition, the optimization of the mentioned factors has been performed through the RSM method to find the optimal conditions. The findings show that RSM and ANN can accurately forecast the adsorption process's the Cu2+ removal efficiency (RE). The maximum values of RE are achieved at the highest value of time (150 min), the highest value of adsorbent dosage (0.008 g), and the highest value of pH (=6). The R2 values obtained were 0.9995, 0.9992, and 0.9996 for ANN modeling of adsorption capacity based on different adsorbent dosages, Cu2+ solution pHs, and different ion concentrations, respectively. The ANN demonstrated a high level of accuracy in predicting the local minima of the graph. In addition, the RSM optimization results showed that the optimum mode for RE occurred at an adsorbent dosage value of 0.007 g and a time value of 144.229 min.
The energy demand of the world is skyrocketing due to the exponential economic growth and population expansion. To meet the energy requirement, the use of fossil fuels is not a good decision, causing environmental pollution such as CO2 emissions. Therefore, the use of renewable energy sources like biofuels can meet the energy crisis especially for countries facing oil shortages such as Pakistan. This review describes the comparative study of biodiesel synthesis for various edible oils, non-edible oils, and wastes such as waste plastic oil, biomass pyrolysis oil, and tyre pyrolysis oil in terms of their oil content and extraction, cetane number, and energy content. The present study also described the importance of biodiesel synthesis via catalytic transesterification and its implementation in Pakistan. Pakistan is importing an extensive quantity of cooking oil that is used in the food processing industries, and as a result, a huge quantity of waste cooking oil (WCO) is generated. The potential waste oils for biodiesel synthesis are chicken fat, dairy scum, WCO, and tallow oil that can be used as potential substrates of biodiesel. The implementation of a biodiesel program as a replacement of conventional diesel will help to minimize the oil imports and uplift the country's economy. Biodiesel production via homogeneous and heterogeneous catalyzed transesterification is more feasible among all transesterification processes due to a lesser energy requirement and low cost. Therefore, biodiesel synthesis and implementation could minimize the imports of diesel by significantly contributing to the overall Gross Domestic Product (GDP). Although, waste oil can meet the energy needs, more available cultivation land should be used for substrate cultivation. In addition, research is still needed to explore innovative solvents and catalysts so that overall biodiesel production cost can be minimized. This would result in successful biodiesel implementation in Pakistan.