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The present study is dedicated to the development of a Coupled Multi Objective Neuro Genetic Algorithm-based model to emulate the NOx, opacity and BSFC (diesel) of an existing single-cylinder four-stroke CI engine operated in various dual-fuel modes with hydrogen operated under EGR of varying thermal signatures. The associated MIMO problem has been transformed into an equivalent MISO model through unique coupling and decoupling algorithms. GA has been used as the search method to optimize the MISO topology. The MISO model substantially reduced the network weight in contrast to its MIMO counterpart thereby ensuring faster convergence and better accuracy with a consequently lower computational footprint and a promising potential to be used as a virtual sensing platform in real-time optimization strategies in engines. The MISO model successfully captured the thermal effects of EGR on the desired outputs.

Hydrogen-fuelled internal combustion engines with near zero emissions and efficiencies exceeding today's port-fuel-injected engines are a potential near term option and a bridge to hydrogen fuel cell vehicles where fuel cell undergoes developments to make it economically viable. Hydrogen with its inherent high flame velocity enables a more isochoric, thus thermodynamically more favorable combustion than conventional diesel engines which undergo a pressure and temperature rise spread over several degrees of crank travel. Hydrogen has an exceptionally wide flammability limits compared to conventional fuels enabling the engine to work with very lean mixtures, thus omitting the necessity of a throttle valve~this ideally suits a diesel engine operation. Its high autoignition temperature helps to realize the working cycles at higher compression ratios and consequent higher brake thermal efficiencies.