Saudi Arabia
Thermal Energy Integration and Optimization in a Biomass-fueled Multi-generation System for Power, Hydrogen, and Freshwater Production
Nov 2025
Publication
This work investigates a biomass-driven multi-generation system designed for simultaneous power freshwater and hydrogen production addressing the interlinked energy-waterenvironment nexus. The configuration integrates Brayton supercritical carbon dioxide (SCO2) organic Rankine cycle (ORC) and thermoelectric generator (TEG) subsystems to maximize utilization of biomass-derived syngas. The recovered energy drives a reverse osmosis (RO) desalination unit for freshwater production and an alkaline electrolyzer for hydrogen generation followed by two-stage compression for storage. Under baseline conditions the system generates 1.99 MW of electricity 9.38 kg/h of hydrogen and 88.6 m3 /h of freshwater with an overall exergetic efficiency of 20.25 % emissions intensity of 0.85 kg/kWh and a payback period of 5.87 years. The Brayton cycle accounts for 49.3 % of the total cost rate while the gasifier exhibits the highest exergy destruction at 46 %. Sensitivity analyses show that varying biomass moisture content (10–30 %) and operating temperatures (700–900 ◦C) significantly influence system performance. Using a data-driven optimization framework that combines artificial neural networks (ANN) and a genetic algorithm (GA) the system’s exergetic efficiency improves to 21.76 % freshwater output rises to 90.96 m3 /h and emissions intensity decreases to 0.877 kg/kWh. Additionally optimization reduces the total cost rate by 2.71 % leading to a payback period of 5.4 years and enhances the system’s overall performance by 12.64 %.
Circular Bioenergy Pathway for Sustainable Hydrogen Production with Carbon Capture: Technical, Economic & Environmental Assessment
Nov 2025
Publication
The accelerating global demand for hydrogen is pushing for renewable and waste derived hydrogen production processes where date palm waste (DPW) has been identified as an available and unexploited agricultural residue that has the potential to be a sustainable source of hydrogen. The current work focuses on developing and evaluating four different process configurations in terms of energy environment and economics for producing hydrogen from DPW using Aspen Plus® simulation tool. Case 1 represents the standalone DPW gasification with CO₂ capture via methanol absorption Case 2 represents the DPW gasification with CaO-based chemical looping for CO₂ capture Case 3 represents the DPW gasification integrated with steam methane reforming (SMR) and methanol-based CO₂ capture and Case 4 represents the DPW gasification integrated with SMR and CaO-based CO₂ capture. Each case was evaluated in terms of syngas composition hydrogen production lower heating value CO₂ captured utility demand process efficiency and H2 production cost. Hydrogen production ranged from 974.55 t/year (Case 1) and 988.83 t/year (Case 2) to 2032.32 t/year (Case 3) and 2048.61 t/year (Case 4). CO₂ capture was also more effective in Case 4 (16929.49 t/year) compared to Case 1 (7676.30 t/year). Process efficiency improved from 33 % in Case 1 to 47 % in Case 2 and from 32 % in Case 3 to further to 55 % in Case 4. Economically Case 1 offered the highest hydrogen production cost ($5.03/kg) followed by Case 2 ($4.77/kg) while Case 3 and Case 4 achieved significantly lower production costs of $2.89/kg and $2.69/kg respectively.
Enhancing Green Hydrogen Forecasting with a Spatio-temporal Graph Convolutional Network Optimized by the Ninja Algorithm
Nov 2025
Publication
In light of increased international efforts to combat climate change sustainable infrastructure is shifting toward green hydrogen produced through renewable-powered electrolysis. Still it is challenging to forecast the production of green hydrogen because environmental and system factors are variable both in time and space. We introduce a new system that utilizes a Spatio-Temporal Graph Convolutional Network (STGCN) and a novel algorithm the Ninja Optimization Algorithm (NiOA) to address this issue. Using the framework binary NiOA performs feature selection while continuous NiOA optimizes both the model architecture and the number of variables in the data simultaneously. It is clear from the research that forecasting results have shown significant improvement. The STGCN model achieved an R2 of 0.8769 and an MSE of 0.00375 whereas the STGCN with NiOA reached an R2 of 0.9815 and an MSE of only 7.48 × 10−8. Due to these improvements adaptive metaheuristics show even greater promise in delivering more accurate forecasting and reduced computational requirements for addressing critical environmental issues. The suggested strategy can be followed repeatedly providing a solid framework for the effective modeling of renewable energy systems and making green hydrogen projects more dependable.
Competition and Equilibrium in Future Global Renewable Hydrogen Trade: A Game-theoretic Analysis
Nov 2025
Publication
Global renewable hydrogen trade is expected to play a key role in decarbonizing future energy systems. Yet hydrogen exporters may deviate from perfectly competitive behaviour to influence prices similarly to the existing fossil fuel market with important implications for consumer welfare and the pace of the energy transition. This study develops a global renewable hydrogen trade model that captures potential strategic interactions among exporters using a Stackelberg game-theoretic framework. The model is formulated as an Equilibrium Problem with Equilibrium Constraints (EPEC) and solved under three alternative equilibria: a profitmaximizing Nash equilibrium a cost-minimizing Nash equilibrium and a welfare-maximizing benchmark representing perfect competition. Results indicate that producers may strategically reduce their export quantities by up to 40 % relative to perfect competition to maximize profits. Such behaviour raises prices to a minimum of 4.5 USD/kg in 2050 across major import markets thereby significantly eroding consumer surplus. Strategic behaviour of dominant exporters also shifts trade flows reshaping the global allocation of hydrogen supply. Sensitivity analysis further reveals that financing costs play a key role in shaping strategic producers’ behaviour with lower financing costs helping to reduce prices and stimulate demand. These findings highlight the implications of imperfect competition in global hydrogen trade and suggest that policy measures may be needed to mitigate potential negative consequences.
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