Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach.

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as Eichhornia crassipes through which biodiesel is generated, and its blends are tested in diesel engines for feasibility. Different models using soft computing and metaheuristic techniques are employed for the accurate prediction of performance and exhaust characteristics. The blends are further mixed with nanoadditives, thereby exploring and comparing the changes in performance characteristics. The input attributes considered in the study comprise engine load, blend percentage, nanoparticle concentration, and injection pressure, while the outcomes are brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbon, and oxides of nitrogen. Models were further ranked and chosen based on their set of attributes using the ranking technique. The ranking criteria for models were based on cost, accuracy, and skill requirement. The ANFIS harmony search algorithm (HSA) reported a lower error rate, while the ANFIS model reported the lowest cost. The optimal combination achieved was 20.80 kW, 2.48047, 150.501 ppm, 4.05025 ppm, and 0.018326% for brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC), and carbon monoxide (CO), respectively, thereby furnishing better results than the adaptive neuro-fuzzy interface system (ANFIS) and the ANFIS-genetic algorithm model. Henceforth, integrating the results of ANFIS with an optimization technique with the harmony search algorithm (HSA) yields accurate results but at a comparatively higher cost.

Experimental analysis of solar powered disinfection tunnel mist spray system for coronavirus prevention in public and remote places.

There is a pressing need to accelerate the development of advanced technologies to prevent Coronavirus in large gatherings. The present system is an integration of tunnel disinfectant spray system and solar setup which utilises the solar energy to power a pump which pushes the required amount of chemical mixture into the nozzles of the spray system in order to eliminate any incoming virus or bacteria on the clothes of a particular person without wetting it. Data was gathered for a mall in New Delhi related to occupancy levels before and after lockdown. A 47% and 35% spike in energy and disinfectant mixture to pump was evaluated on weekends. A 70% reduction in operating cost was registered for solar based system in comparison to non-solar setup. Optimum conditions by considering efficiency and cost effectiveness evaluated are 8 number of nozzles, nozzle angle 55 degrees spray pressure 200 bar, and pressure 200 bar.