Unlocking the potential of transesterification catalysts for biodiesel production through machine learning approach.

The growing demand for fossil fuels has motivated the search for a renewable energy source, and biodiesel has emerged as a promising and environmentally friendly alternative. In this study, machine learning techniques were employed to predict the biodiesel yield from transesterification processes using three different catalysts: homogeneous, heterogeneous, and enzyme. Extreme gradient boosting algorithms showed the highest accuracy in predictions, with a coefficient of determination accuracy of nearly 0.98, as determined through a 10-fold cross-validation of the input data. The results indicated that linoleic acid, behenic acid, and reaction time were the most crucial factors affecting biodiesel yield predictions for homogeneous, heterogeneous, and enzyme catalysts, respectively. This research provides insights into the individual and combined effects of key factors on transesterification catalysts, contributing to a deeper understanding of the system.

Optimization of iodine number of carbon black obtained from waste tire pyrolysis plant via response surface methodology.

Recovered carbon black (RCB) obtained from a tire pyrolysis plant was subjected to chemical and thermal treatments for application as a filler in rubber compounds. Carbon black was chemically treated with nitric acid by varying the temperature, time, and chemical-to-carbon black ratio. The iodine number was optimized using response surface methodology (RSM) and the Design Expert software. To increase the iodine number, the Box-Behnken design was utilized to optimize three parameters: temperature (30-50 °C), time (6-24 h), and ratio of carbon black to chemical (0.25-1.0 g/mL). Under optimal conditions, the surface area increased, and RCB was upgraded to commercial carbon black N330. RSM analysis indicted that the iodine number was maximized (117.34 mg/g) after treatment at 46.74 °C for 23.24 h using a carbon black/chemical ratio of 0.76 g/mL. The simulated data were experimentally validated by analyzing RCB_ EQ, which yielded an iodine number of 119.12 mg/g. The content of most heavy metals in RCB decreased by more than 90%, whereas the sulfur and chlorine content decreased by 43.27% and 53.96%, respectively. Based on thermogravimetric analysis, the RCB_13 carbon black additive was eliminated at temperatures of 620-800 °C.

Multi-objective atom search optimization of biodiesel production from palm empty fruit bunch pyrolysis.

In this study, palm empty fruit bunch (PEFB) pyrolysis, bio-oil improvement, and generating electricity were all simulated using Aspen plus. This research employed a kinetic reactor for pyrolysis at 500 °C based on 1,000 tons of PEFB per day. The simulation results indicated that 1 kg/hr. PEFB generated 0.11 kg/hr of char, 0.21 kg/hr of gas, and 0.67 kg/hr of bio-oil, which is in good agreement with literature. The relationship between biodiesel yield, CO2 emissions, and utility costs was then investigated the effect of the distillate-to-feed ratio of biodiesel distillation, heat exchanger temperature, and the flash drum pressure from the process simulation by using central composite design (CCD). The coefficient of determination (R2) values for biodiesel yield, CO2 emissions, and utility costs were 0.9940, 0.9941, and 0.9959, respectively, which was a reason for the excellent model fitting. The optimum response (the biodiesel yield, the CO2 emission, and the utility cost) was obtained at 5,562.73 kg/hr, 33,696.55 kg/hr, and 2,953.99 USD/hr., respectively, with optimum conditions for the distillate-to-feed ratio of 0.899999, temperature of 56.0356 °C and pressure of 18.1479 bar. After that, a quadratic polynomial equation from the RSM was employed as the fitness function to evaluate the fitness value of the multi-objective optimization (MOO) by atom search optimization (ASO) to maximize biodiesel yield and minimize the CO2 emissions and utility costs. The ASO performance was generated into the Pareto optimal solution of 200 generations. The optimal CCD was then compared with the ASO results. It was found that the ASO could reduce CO2 emissions by 1.33% and reduce utility costs by 5.03% while increasing biodiesel yields by 7.01%. It can be observed that the ASO was more efficient at finding parameters than the CCD.

Combined heat and power plant using a multi-objective Henry gas solubility optimization algorithm: A thermodynamic investigation of energy, exergy, and economic (3E) analysis.

The principal context of this study was a combined heat and power plant (CHPP) system, with the aim of conducting the multi-objective optimization (MOO) of an energy, exergy, and economic (3E) analysis. To meet rising energy demands, optimal operational conditions for CHPPs are required. Enhancements to plant equipment and improvements in plant design are critical. CHPP design has its basis in the first law of thermodynamics; the losses from such systems are therefore most accurately determined via exergy analysis. Energy quality can also be assessed using exergy analysis. Consequently, it is possible for the designers of thermodynamic systems to apply the findings to achieve improved efficiencies. The economic aspect of CHPP optimization is also critical because the structure is highly complex. This study therefore makes use of a Henry gas solubility optimization (HGSO) algorithm in a CHPP base case situation to achieve MOO. In this particular CHPP system, the respective enthalpy and exergy efficiencies were increased in the case of the boiler (7.22% and 7.21%), the turbogenerator (4.52% and 6.84%), and the condenser (3.06% and 31.37%). In this study, four scenarios are proposed, whereby the design of a heat exchanger network (HEN) aims to optimize energy savings and economic performance through analysis of the profits generated through electricity and steam production. A payback period of around two to three years was reported, where the cost increase under optimal conditions was found to be 0.3824%. The results demonstrate clearly that the tested techniques may be appropriate in practical scenarios when enhancing CHPP performance in the context of the base case.