Search

This paper presents a ground-breaking design for a multigeneration system capable of simultaneously producing electricity, hydrogen, and cooling loads. This research advances sustainable energy systems by introducing an innovative design that optimally utilizes waste heat and integrates biomass gasification with advanced thermodynamic cycles. It also provides a model for future studies on carbon emission reduction and improved efficiency. The proposed system effectively harnesses waste heat from the Brayton cycle to drive the supercritical carbon dioxide cycle, steam Rankine cycle, absorption refrigeration, and proton exchange membrane electrolyzer. This approach improves overall efficiency and offers a promising solution for integrated energy generation. Additionally, employing the sCO2 cycle provides high thermal efficiency, cost-effectiveness, and lower environmental impacts compared to traditional power generation methods. Extensive evaluations, including techno-economic and environmental analyses, confirm the system's practicality and potential for future commercial application. Additionally, a parametric investigation of five essential design parameters provides important insights into the system's performance and flexibility. Analysing the proposed system determined that the gasifier-Bryton unit has the highest irreversibility and cost rate among other subsystems. A novel approach combining artificial neural networks (ANN) with a non-dominated sorting genetic algorithm II (NSGA-II) has been developed to optimize the system, substantially reducing computational time and costs associated with system performance analysis. According to the three-objective optimization, the system in the optimal operating mode provides 45.89 kg/h of hydrogen with an exergy efficiency of 33.15 % and a total cost rate of 159.5 $/h. After the optimization process, significant achievements have been observed, including a 5.02 % improvement in exergy efficiency, an increase of 7.29 kg/h of hydrogen production, and a decrease of 0.1 ton/MW in the CO2 emission index.

The study of boundary layer flow (BLF) with heat-mass transfer of binary chemical processes and nanofluids (NF) over a wedge is essential for improving heat transfer and reaction kinetics in applications including processing of material technologies, chemical reactors, and energy-efficient cooling mechanisms. This paper examines the entropy optimized BLF of silver Ag− water based nanofluid with binary chemical species over a wedge surface. The Tiwari-Das model is executed in this model which account the load of Ag− nanomaterials. The flow of NF over a moving wedge subject to favorable and adverse pressure differential is addressed by Naiver-Stokes equation. This model accounts the homogeneous heat reaction, viscous dissipation, joule heating and thermal radiations. The dimensionless equations for flow, for heat, and concentration are formulated and solved numerically using the fourth ordered Rung-Kutta approach. The findings suggest that fluid concentration is lowered with a rise in Schmidt number and homogenous chemical reaction. Thermal distribution improve with heterogonous reaction, magnetic parameter and deteriorate with wedge parameter. The skin friction rises from 25.277 % to 26.455 % with a material load of 3 % and magnetic parameter. The Nusselt decline with a radiative parameter from 10.984 % to 2.9748 % when particle load of 3 % is accounted.

Indeed, nanoliquids have acquired substantial consideration in heat transference field because of their inimitable thermal attributes and favorable application likelihoods. In contrast to orthodox liquids, the haphazard movement of nanoparticles within nanoliquid strengthens fluid turbulence, accomplishes superior thermal effectiveness and declines thermal resistance. Nanoliquids have ample utilization, for illustration, solar energy, electronic chips, automotive radiators and heat exchangers etc. This communication reports chemically reactive electro-magnetized nanomaterial dissipative flow confined by rotating cone. Flow expressions include thermo-solutal buoyancy, varying viscosity and magneto-hydrodynamics. Radiative heat, thermophoresis, viscous dissipation, Brownian diffusion, thermal source and first order chemical reaction are pondered to model transport expressions. Relevant variables are introduced to transfigure partial differential mathematical expressions to mathematical ordinary ones. Numerical outcomes for non-dimensional mathematical expressions are reported via bvp4c algorithm in MATLAB. The comprehensive results featuring dimensionless quantities are explored through graphs and arithmetic representations. It is evaluated that escalating values of variable viscosity, Prandtl number and unsteady parameter decline temperature but temperature is improved as a consequence of progressive variation in radiation parameter, Eckert number, thermophoresis parameter, heat generating and Brownian diffusive variables. The study is relevant to cooling industry, electroconductive, thermal collector and nano-materials processing.

The current study aims at modeling the solubility of anti-cancer agents in supercritical carbon dioxide (SC-CO2). An extensive databank, including 893 measured samples for 33 anti-cancer agents were collected from the literature, covering extensive ranges of operating conditions. Eight density-based empirical models were firstly employed to correlate the collected data. After adjusting their constant coefficients, four of them provided satisfactory estimations, with total average absolute relative errors (AAREs) below 10 %. A novel six-parameter empirical correlation was also proposed, with input factors optimized based on the Pearson coefficient analysis. This correlation produced satisfactory results for the analyzed drugs, achieving a total AARE of 7.71 %. Afterward, a generalized and unified model was built using the intelligent method of gaussian process regression (GPR). For the testing data, this model showed excellent results with AARE and R2 values of 2.90 % and 99.87 %, respectively. Furthermore, its estimations for all anti-cancer agents outperformed the empirical correlations significantly. Both empirical and intelligent models accurately described the physical behavior of anti-cancer agents’ solubility in SC-CO2 under various conditions. Subsequently, the most effective factors on the performances of the models were recognized through a sensitivity analysis.

This paper presents a novel solar-powered multi-generation system (MGS) integrated with a fuel cell, designed to enhance both sustainability and operational reliability. A significant limitation of solar energy is its intermittency, as sunlight is only available during specific hours of the day. To address this constraint, hydrogen energy is incorporated into the system to facilitate continuous operation through the fuel cell. The proposed MGS efficiently utilizes waste heat recovery cycles to simultaneously produce electricity, fresh water, cooling, and heating. The system consists of parabolic trough solar collectors, a steam Rankine cycle, an organic Rankine cycle, an absorption chiller, a reverse osmosis desalination unit, and a fuel cell coupled with an organic Rankine cycle-thermoelectric generator. Upon validating the primary components, the system is thoroughly evaluated in terms of energy, exergy, and economic performance, and a parametric study is conducted to assess the influence of key operational parameters. The analysis identifies the solar cycle as having the highest irreversibility, accounting for 55.2 %, and the highest cost rate, contributing 44.1 % among the subsystems. To optimize the system's performance, an artificial neural network is integrated with a multi-objective particle swarm optimization algorithm to reduce computational time from approximately 16 h to 4 min. Finally, under optimal conditions, the system achieves an exergy efficiency of 31.69 %, freshwater production of 19.53 kg/s, cooling production of 441.6 kW, and a total cost rate of $87.2/h.