Using bone char for contaminated wastewater treatment and soil remediation is an intriguing approach to environmental management and an environmentally friendly way of recycling waste. The bone char remediation strategy for heavy metal-polluted wastewater was primarily affected by bone char characteristics, factors of solution, and heavy metal (HM) chemistry. Therefore, the optimal parameters of HM sorption by bone char depend on the research being performed. Regarding enhancing HM immobilization by bone char, a generic strategy for determining optimal parameters and predicting outcomes is crucial. The primary objective of this research was to employ artificial neural network (ANN) technology to determine the optimal parameters via sensitivity analysis and to predict objective function through simulation. Sensitivity analysis found that for multi-metals sorption (Cd, Ni, and Zn), the order of significance for pyrolysis parameters was reaction temperature > heating rate > residence time. The primary variables for single metal sorption were solution pH, HM concentration, and pyrolysis temperature. Regarding binary sorption, the incubation parameters were evaluated in the following order: HM concentrations > solution pH > bone char mass > incubation duration. This approach can be used for further experiment design and improve the immobilization of HM by bone char for water remediation.
Microalgae have great potential in producing energy-dense and valuable products via thermochemical processes. Therefore, producing alternative bio-oil to fossil fuel from microalgae has rapidly gained popularity due to its environmentally friendly process and elevated productivity. This current work aims to review comprehensively the microalgae bio-oil production using pyrolysis and hydrothermal liquefaction. In addition, core mechanisms of pyrolysis and hydrothermal liquefaction process for microalgae were scrutinized, showing that the presence of lipids and proteins could contribute to forming a large amount of compounds containing O and N elements in bio-oil. However, applying proper catalysts and advanced technologies for the two aforementioned approaches could improve the quality, heating value, and yield of microalgae bio-oil. In general, microalgae bio-oil produced under optimal conditions could have 46 MJ/kg heating value and 60% yield, indicating that microalgae bio-oil could become a promising alternative fuel for transportation and power generation.
This study was set up to model and optimize the performance and emission characteristics of a diesel engine fueled with carbon nanoparticle-dosed water/diesel emulsion fuel using a combination of soft computing techniques. Adaptive neuro-fuzzy inference system tuned by particle swarm algorithm was used for modeling the performance and emission parameters of the engine, while optimization of the engine operating parameters and the fuel composition was conducted via multiple-objective particle swarm algorithm. The model input variables were: injection timing (35-41° CA BTDC), engine load (0-100%), nanoparticle dosage (0-150 μM), and water content (0-3 wt%). The model output variables included: brake specific fuel consumption, brake thermal efficiency, as well as carbon monoxide, carbon dioxide, nitrogen oxides, and unburned hydrocarbons emission concentrations. The training and testing of the modeling system were performed on the basis of 60 data patterns obtained from the experimental trials. The effects of input variables on the performance and emission characteristics of the engine were thoroughly analyzed and comprehensively discussed as well. According to the experimental results, injection timing and engine load could significantly affect all the investigated performance and emission parameters. Water and nanoparticle addition to diesel could markedly affect some performance and emission parameters. The modeling system could predict the output parameters with an R2 > 0.93, MSE