4-E analysis and multiple objective optimizations of a novel solar-powered cogeneration energy system for the simultaneous production of electrical power and heating.

Owing to its natural and rich advantages, exploration of solar energy technology has become increasingly popular in recent years to counter the growing crude oil prices. However, its universal adoption is still limited, not only due to environmental restrictions but also due to lower overall efficiency. Rankine cycle is optimised to conduct 4-E (Exergy, Energy, Economic and Ecological) analysis. Furthermore, three sets (R-113, R-11, and R-1233zd) of refrigerants are prioritised and ranked on the basis of 4-E analysis as outcomes. The contemporary study addressed all critical factors and explains the impact of solar irradiance, mass flow rate of molten salt and steam, turbine inlet pressure, and turbine inlet temperature which are eventually weighed and prioritised using combined multi-criteria decision making (MCDM) techniques. The energy efficiency, exergetic efficiency, power/ cost of electricity, and ecological emissions are taken as the indicators of the combined cycle, respectively. The energy efficiency of the hybrid system is improved to 75.07% after including cogeneration cycle, with an increment of 54.58%. In comparison to conventional thermal powerplant setups, the power/cost of electricity and ecological efficiency have been reduced by 68% and upgraded by 16%, correspondingly. Direct normal radiation is the most critical factor followed by turbine inlet temperature. Further, the result indicates that maximum exergy destruction that occurs in the central receiver declines to 39.92%, followed by heliostat and steam turbine which was 27% and 9.32% respectively. In conclusion, the hybrid cycle can furnish cheaper electricity, with lower carbon imprint in sustainable manner with better efficiency.

Experimental investigation and multi-performance optimization of the leachate recirculation based sustainable landfills using Taguchi approach and an integrated MCDM method.

Landfill leachates contain harmful substances viz. chemicals, heavy metals, and pathogens, that pose a threat to human health and the environment. Unattended leachate can also cause ground water contamination, soil pollution and air pollution. This study focuses on management of leachate, by recirculating the rich, nutrient-filled fluid back into the landfills, turning it to a bioreactor, thereby maximising the performance parameters of landfills favourable for electricity production by the waste to energy plants. This study demonstrates a sustainable alternative method for utilising the fluid, rather than treating it using an extremely expensive treatment process. Further, it also experimentally investigates the effect of varying levels of five input parameters of the landfill including waste particle size, waste addition, inorganic content in waste, leachate recirculation rate, and landfill age, each at five levels, on the multiple performance of the landfill using Taguchi's L25 standard orthogonal array. Experimental results are analysed using an integrated MCDM approach i.e. MEREC-PIV method and statistical techniques such as analysis of mean (ANOM) and analysis of variance (ANOVA). The results indicate that the optimal setting of the input parameters is waste particle size at 9 ppm, waste addition at 80 Ktoe, inorganic content in waste at 2%, leachate recirculation rate at 250 l/day and landfill age at 3 years. Further, inorganic content waste is found to be the most significant parameter for the multiple performance of the landfill. This study presents a novel approach to produce input parameters for power plants which may enhance their profitability and sustainability.

Modelling of compression ignition engine by soft computing techniques (ANFIS-NSGA-II and RSM) to enhance the performance characteristics for leachate blends with nano-additives.

Integrating nanoparticles in waste oil-derived biodiesel can revolutionize its performance in internal combustion engines, making it a promising fuel for the future. Nanoparticles act as combustion catalysts, enhancing combustion efficiency, reducing emissions, and improving fuel economy. This study employed a comprehensive approach, incorporating both quantitative and qualitative analyses, to investigate the influence of selected input parameters on the performance and exhaust characteristics of biodiesel engines. The focus of this study is on the potential of using oils extracted from food waste that ended up in landfills. The study's results are analysed and compared with models created using intelligent hybrid prediction approaches including adaptive neuro-fuzzy inference system, Response surface methodology-Genetic algorithm, and Non sorting genetic algorithm. The analysis takes into account engine load, blend percentage, nano-additive concentration, and injection pressure, and the desired responses are the thermal efficiency and specific energy consumption of the brakes, as well as the concentrations of carbon monoxide, unburned hydrocarbon, and oxides of nitrogen. Root-mean-square error and the coefficient of determination were used to assess the predictive power of the model. Comparatively to Artificial Intelligence and the Response Surface Methodology-Genetic Algorithm model, the results provided by NSGA-II are superior. This is because it achieved a pareto optimum front of 24.45 kW, 2.76, 159.54 ppm, 4.68 ppm, and 0.020243% for Brake Thermal Efficiency, Brake Specific Energy Consumption, Oxides of nitrogen, Unburnt Hydro Carbon, and Carbon monoxide. Combining the precision of ANFIS's prediction with the efficiency of NSGA-optimization II's gives a reliable and thorough evaluation of the engine's settings. The qualitative assessment considered practical aspects and engineering constraints, ensuring the feasibility of applying the parameters in real-world engine applications.