## 1 Introduction
At intermediate loads of 7-9 bar IMEP, enhanced combustion efficiency was attained along with reduced particulate matter (PM) and NOx emissions; however, this was accompanied by an increase in CO and UHC emissions [27]. [PERSON], [PERSON] et al. used an artifice neural network (ANN) to figure out the effect of the operational parameters, examining the effects of both intake temperature and pressure at the intake valve closing (IVC), as well as the start of injection (SOI) of diesel fuel, on the performance of a single-cylinder, heavy-duty (methane/diesel) dual-fuel RCCI engine operating at a 9.4 bar IMEP load. The outcome suggested that the fractional factorial method can generate a proper dataset for training ANN. Moreover, using artificial neural networks as an effective tool facilitates the prediction of the occurrence of misfires, excessive combustion noise, and the favored range of engine load [28]. [PERSON] et al. conducted a numerical investigation using CONVERGE software to analyze the impact of various piston bowl geometries on the performance and emissions of a gas/diesel RCCI engine operating at a medium load. The study utilized three distinct piston bowl geometries--the cylindrical, stock design, and bathtub-shaped bowl--while maintaining a fixed compression ratio (CR) of 16.1:1. A double injection strategy was employed to inspect the depth of the piston bowl, the existence of a beveled ring-land, and the engine speed on the RCCI engine's performance. The finding revealed that the piston bowl shape at low speeds has no impact on the combustion of the RCCI engine, but it has a considerable impact at high engine speeds. Increasing the engine speed caused a reduction in in-cylinder temperature and pressure, which in turn resulted in a delay in combustion phasing. As a result, both the ringing intensity (RI) and the gross indicated efficiency (GIE) decreased, while emissions of CO and UHC increased. The optimum piston bowl shape is the bathtub-shaped design since it provides the best engine performance and lowers emissions at high speeds. Moreover, increasing the depth of the piston bowl to 1 mm or utilizing a chamfered ring-land by increasing the chamfer size up to 3 mm can increase the gross indicated efficiency (GIE) and minimize both CO and UHC emissions [29]. [PERSON] et al. conducted a study on optimizing the geometry of the engine's combustion chamber by utilizing the NSGA-II with a kriging-based meta-model for a natural gas/diesel RCCI engine. The results showed that the indicative efficiency and NOx emissions were slightly improved, and CO and HC emissions were reduced by 33.55% and 56.4%, respectively [30]. In their study, [PERSON] et al. optimized the diesel injection and operating parameters of a natural gas/diesel engine using the NSGA-II algorithm. The aim of this study was to minimize indicated specific fuel consumption (ISFC) and emissions of NOx and CH4. The soot emission outcome meets the EURO VI emission standard. However, only some solutions fully comply with the EURO V NOx emission standard [31]. [PERSON] et al. investigated the optimization of injection strategies for gasoline and diesel engines to minimize exergy destruction, maximize exergy efficiency, and reduce the noise index. An artificial neural network and the NSGA-II algorithm were employed to investigate optimal solutions. Exergy efficiency increases by up to 2%, and exergy destruction is reduced by approximately 2.3% compared with the base case. Reductions in soot and NOx emissions of 35% and 40%, respectively, were achieved [32]. [PERSON] et al. conducted an optimization study to determine the impacts of fuel rates, EGR, and SOI on the gasoline/diesel RCCI engine by applying the NSGAII algorithm. They found an enhancement of indicative and second-low efficiencies of 1.56% and 1.25%, respectively, along with a 16.67% reduction in NOx concentration [33].