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Reactivity-controlled compression ignition engines have garnered significant interest for their potential to deliver enhanced performance, particularly in environmental aspects. This study investigates the performance of such engines within a co-generation framework for simultaneous power and cooling production. Low reactivity fuel for the engine is derived from solar steam reforming of methanol, with waste heat recovered through a supercritical CO 2 cycle and an absorption refrigeration cycle. The designed configuration undergoes comprehensive analysis encompassing thermodynamic, economic, and exergoenvironmental assessments, including parametric analysis. Optimal engine performance is evaluated through computational fluid dynamics, thermodynamic, and exergoenvironmental analyses across various syngas compositions and reforming conditions. Results indicate that a syngas portion of 60% at a reforming temperature and CH3OH to H2O ratio of 200 °C and 1.2 yields optimal engine performance. Besides, the inlet temperature of the CO 2 turbine exhibits the greatest impact on co-generation performance. At the optimal state, co-generation's power and cooling load generation reach 467.8 kW and 225 kW, with a unit cost of 53.89 $/GJ, an exergy efficiency of 38.14%, a sustainability index of 1.622, and an exergoenvironmental impact index of 1.607. • A RCCI engine's performance is investigated in a co-generation. • The LRF is supplied by a steam reforming of methanol. • The engine's best performance is achieved at syngas portion of 60%. • The optimal exergy efficiency, SI, and EII are attained 38.14%, 1.622, and 1.607. [ABSTRACT FROM AUTHOR]

This work highlights the application of two types of artificial neural networks namely PSO-RBF and CSA-LSSVM for prediction of equilibrium adsorption of sulfur compound in liquid phase of hydrocarbon solution of isotherm batch systems. The precision and reliability of developed models were investigated by various graphical and statistical approaches. Initial sulfur concentration, utilized adsorbent weights, molecular weights and densities of solvent and solute, average size of adsorbent particle, Si/Al ratio of adsorbent and temperature were used as input parameters of models and the amount of adsorption was considered as the output. Results show that the predictions of CSA-LSSVM model are a little more precise and reliable compared to the outcomes of PSO-RBF model. The overall values of R 2 and AARD% for CSA-LSSVM model were 0.9920 and 0.55 that show precision and robustness of the applied model. [ABSTRACT FROM AUTHOR]