Integration of renewable energy and waste heat resources could effectively reduce emissions and the production cost in methane production power plants. The objective of this study is to conduct a technoeconomic analysis of an Iraqi methanol production facility using a combination of energy resources of waste gas from Al-Fallujah white cement factory, solar and wind energy. It is hypothesized that the carbon dioxide present in the flue gas could be extracted using solar and wind turbine energy in a carbon capture unit in the hydrogen plant. Methanol fuel is then produced in the methanol plant from the combined sources. The amount of energy and the number of solar panels or wind turbines (WT) needed to supply this energy requirement were estimated using the Engineering Equation Solver (EES), and then the environmental impact of the methanol plant was assessed. The efficiencies of renewable energy PV, wind turbines, methanol plants, and methanol fuel were predicted as 21%, 35%, 16.26%, and 58.72%, respectively. The electrolyzers' efficiency was 78.2% at their ideal density of 2.2 kA/m$^2$. With a production capacity of 34,530 million tons of methanol, the total cost to operate the plant for 30 years for each of the PV plants and WT was found to be \$9.46 billion and \$5.291 billion, respectively. This translates to 0.4131 \$/kg methane for the PV plant and 0.2413 \$/kg methane for the wind power plant. In terms of the environment, there is a daily 3,894 tons of collected CO$_2$ emissions and 3,306 tons of mitigation. The results show that the current facility can compete with facilities that produce clean synthetic fuel.

Mainstream frameworks—such as the policy cycle, the Multi-Level Perspective (MLP), the Advocacy Coalition Framework (ACF), and Technological Innovation Systems—help interpret energy transitions but often stop at theoretical narratives and remain insufficiently operational at the subnational level. This article adapts the Kaleidoscope Model (KM) (16 variables across agenda setting, design, adoption, implementation, and evaluation) into a quantified diagnosis-to-action framework directly linked to evidence-based policy (EBP) recommendations. The Lampung case study integrates thematic analysis, social network analysis (SNA), and regression to map leverage variables such as actor support, data readiness, cross-sector coordination, and elite attention. Variable scores reveal a dependence on external momentum—regulations and crises—paired with still-fragile internal capacity. Integrating the KM with EBP yields an operational policy package: the establishment of a regional energy-transition coordination body, an integrated energy data system, and performance-based fiscal incentives. The study’s main contributions are fourfold: first, operationalizing variables into measurable indicators; second, linking diagnosis to action pathways through institutional design and a policy mix; third, prioritizing interventions with quantitative evidence via SNA and regression; and fourth, enabling iterative policy recalibration. These findings provide a contextual, adaptive, and accountable roadmap for new and renewable energy (NRE) reform for regional governments.

In this study, the effect of the collector–chimney junction geometry on the performance of a Solar Chimney Power Plant (SCPP) is investigated using numerical simulations based on a two-dimensional axisymmetric model with the standard k–ε turbulence model, coupled with the Discrete Ordinates (DO) radiation model. An SCPP with a collector diameter of 3 m and a chimney height of 3 m is considered. Several junction configurations are analyzed by varying the junction inclination angle, arc radius, and fillet radius. The results show that the junction configuration with a collector inclination angle of 20° and a fillet radius of 14 cm provides the best performance among the inclined cases, enhancing the mass flow rate by approximately 3.2% and increasing the power density by about 15% compared to the same inclination angle without a fillet. In addition, the configuration incorporating an 18 cm arc radius combined with a 14 cm fillet radius increases the mass flow rate by around 1.7% and improves the power density by approximately 7.5% compared to the corresponding arc configuration without a fillet Furthermore, a comparative analysis between configurations with and without a fillet radius reveals that the introduction of a fillet significantly improves the overall system performance, yielding increases of up to 8% in mass flow rate and about 5% in power density. These enhancements are attributed to smoother airflow transitions and more efficient inlet acceleration at the collector–chimney junction, leading primarily to improved aerodynamic performance of the solar chimney system.

Given the extreme scarcity of water in arid regions, innovative solutions are essential to provide potable water. Among these solutions, solar desalination technology stands out, using sunlight to evaporate saltwater and then condense it to convert it into fresh water. However, conventional solar desalination systems face challenges related to production efficiency, which is affected by factors such as solar radiation intensity, wind speed, and temperature. This research aims to improve the efficiency of these systems using mesh cotton. Studies have shown that mesh cotton absorbs more sunlight, increasing the evaporation rate, while wood acts as a thermal insulator and enhances the system's efficiency. The experiment revealed a 37% increase in water production from the improved distillate. The conventional system produced 1,300 ml of distilled water per day, while the improved system produced 1,742 ml per day. These improvements indicate that the use of readily available materials can significantly improve the efficiency of solar desalination systems, helping address water scarcity in arid regions.

This study presents a comprehensive numerical analysis aimed at determining the enhanced combustion rate, burning velocity, and laminar flame speed of premixed LPG/air mixtures. Different values of equivalence ratios (ER) were considered between 0.6 and 1.4. The analyses were conducted at initial conditions of 1.0 atm and 300 K inside a horizontal cylindrical combustion chamber (CCC). Simulations were performed using ANSYS Fluent and Chemkin USC Mechanics 2.0 software, which effectively predicted flame characteristics. The results indicate that the stoichiometric mixture gives the highest extended laminar flame velocity of 288.88 cm/s, followed by the lean mixture, ER = 0.6, with 84.1 cm/s, and the rich mixture, ER = 1.4, with 118.66 cm/s. The observed combustion rates of the stoichiometric, lean, and rich mixtures were 14.9, 37, and 18.5 cm/s, respectively. Also, the laminar burning velocity for pure propane and pure butane at different ERs, of 0.6, 0.8, 1.0, 1.2, and 1.4, were 19.4, 36.9, 45, 42.1, and 25.1 for propane mixtures, and 14.8, 29, 36.5, 33.4, and 18.4 for butane mixtures, respectively. For the same aforementioned ER, the measured laminar flame velocities were 87.55, 200.9, 274.1, 238.9, and 116.67 cm/s, respectively, and the laminar combustion velocities were 15.2, 28.7, 35.5, 33, and 18.2 cm/s, respectively, with a 2.8% gain margin. Moreover, for naphtha fuel vapors, laminar combustion velocities of 32.16, 41.2, 49.45, 46, and 28.6 cm/s for ERs of 0.6, 0.8, 1, 1.2, and 1.4. The numerical results of the ILPG (Iraq liquefied petroleum gas) show that the maximum stretched laminar flame speed reached 288.88 cm/s at ER = 1.0, while with ER = 0.6, it is 84.1 cm/s, and with ER = 1.4, it is 118.66 cm/s. Compared to ILPG, propane, and butane, Naphtha burns faster.

Cold chain logistics systems are essential for preserving temperature-sensitive goods, yet they face increasing operational and environmental challenges due to thermal constraints, dynamic delivery demands, and route uncertainties. This study proposes a hybrid soft computing approach that integrates Fuzzy Logic, Genetic Algorithm (GA), and Ant Colony Optimization (ACO) to optimize energy distribution within thermally-constrained logistics networks. A mathematical model is formulated to minimize a multi-objective cost function that includes total energy consumption, travel time, and penalties from temperature deviations, all subject to vehicle capacity, time window, and thermal stability constraints. The Fuzzy Logic module evaluates uncertainties related to product sensitivity and delivery urgency, assigning adaptive penalty weights to guide the GA-based global search. Subsequently, the ACO layer enhances routing solutions through pheromone-driven refinement. Simulation experiments were conducted over 20 randomized testbeds, with the proposed hybrid model consistently outperforming mono-algorithmic benchmarks. On average, the model reduced energy usage by 12.6%, lowered temperature violations by 28.3%, and increased on-time delivery rate by 15.1% compared to standard GA or ACO approaches. These results demonstrate the model’s capability to generate robust and efficient routes under real-world constraints. In practical terms, logistics providers can adopt this framework to achieve substantial cost savings, reduce spoilage of perishable goods, and enhance environmental sustainability. Moreover, the model is scalable and can be adapted to integrate IoT-based monitoring and renewable energy systems in future implementations.

As part of the study of the possibilities of using hydrogen as an alternative energy source, in particular in aspects of its underground storage, it is necessary to evaluate its interaction with host rocks. This article describes the initial results of experimental studies on carbonate rocks, specifically clayey limestones, when injecting hydrogen together with methane under given reservoir conditions typical for underground gas storage facilities, paying special attention to the assessment of changes in the pore space. The paper compares the method of computed tomography, which analyzes discrepancies in the attenuation of X-ray radiation by various rock components, and nuclear magnetic resonance relaxation, based on the phenomenon of resonant absorption of electromagnetic field energy by matter caused by nuclear paramagnetism. As a result of the interpretation of the analysis, it was shown that the overall and effective porosity remain stable as the values decreased for the tested samples by 0.1%, which indicates that hydrogen does not significantly affect the reservoir properties. An important result was the assessment of clay porosity, according to nuclear magnetic resonance relaxation calculations, its value increased by 2 times (from 0.15% to 0.28%) after exposure in the hydrogen-methane mixture, indicating the need to control the state of the overlapping clay strata and their integrity. These initial studies can be used in oil and gas field practice.

The increasing integration of distributed renewable resources such as photovoltaic (PV), wind, and battery energy storage systems (BESS) introduces both opportunities and challenges in managing hybrid microgrids. This study develops a forecast-integrated load control framework for a six-classroom microgrid supplied by PV, wind, BESS, Diesel Generator (DG), and the utility grid. Short-term forecasts of renewable generation and building load are embedded in a Particle Swarm Optimization (PSO) model to generate 15-minute scheduling decisions. The objective function minimizes grid import, peak demand, and operating cost while maintaining comfort and technical constraints. The framework was experimentally validated using IoT-enabled sensing and Supervisory Control and Data Acquisition (SCADA)-based control at an educational facility. Results demonstrate forecasting accuracy above 92%, a 31% reduction in peak demand, a 28% increase in PV self-consumption, and a 21.5% reduction in energy costs compared with rule-based and GA-based strategies. Sensitivity and robustness analyses confirm stable performance under ±15% forecast deviation. The proposed framework provides a scalable, adaptive, and cost-effective strategy for renewable-rich microgrids, offering direct implications for smart campus and commercial energy management.

In the present study, the Archimedes turbine is employed at low heads at Barrages and regulators in Anbar Province, Iraq. It is a small hydropower station that is suitable for application because it does not require high storage water (high head) in a Barrage. The 3D numerical model (ANSYS) has been employed for simulating and determining the power produced from the turbine in the Barrages. The physical model is applied to determine the optimal inclination angles of the shaft turbine (α) with a suitable water flow rate. This physical model was applied after conducting a set of tests that included different inclination angles of the shaft turbine (30°, 35°, 40°, 45°) and different discharges also reached the highest efficiency of 89.4% for the optimal angle of the model 35°. The results show Ramadi and Fallujah Barrages are the best investments in generating power because the discharges of these barrages continue throughout the year. Using Archimedes screw turbine as clean energy technology is an effective method and can be used to generate clean power without the need for large storage water because it appropriates the hydrologic conditions of the Euphrates River in Iraq. This study supports renewable energy, improves energy access, and contributes to energy efficiency and energy security for local communities.