Use of end-of-life photovoltaic (PV) modules is increasing rapidly, creating both environmental challenges and opportunities for improved circular design. This study presents a digital twin-driven design framework that supports low-cost recycling and reuse of solar panel components using only basic mechanical and electrical laboratory setups. The approach integrates 3D computer-aided modeling, guided disassembly, and preliminary experimental testing to establish an accessible methodology for sustainable energy equipment design.

A 3D digital twin of a standard PV module is developed using computer-aided design and simulation tools to visualize structural layers, simulate disassembly steps, and assess material separation pathways. Key components, including the aluminum frame, tempered glass, and encapsulating polymers, are evaluated for recyclability using simple mechanical characterization techniques. In parallel, partially functional solar cells are recovered from decommissioned modules, reconnected, and tested in the electrical laboratory to observe voltage–current behavior and basic stability.

The combined digital–physical workflow demonstrates how digital twin technologies can guide sustainable design for disassembly and support circular-economy practices in renewable energy systems. The proposed model is low-cost, replicable, and suitable for educational environments, offering a practical link between digital design, materials engineering, and renewable energy. This early-stage work highlights the potential of digital twins to accelerate PV recycling and extend solar panel lifecycles through intelligent design methodologies.

Response Surface Methodology (RSM) combined with Central Composite Design (CCD) represents a powerful statistical approach for materials optimization in energy storage systems [1,2]. RSM has been extensively applied in materials science to efficiently explore design spaces and identify optimal synthesis conditions, reducing experimental burden while maintaining rigor [2]. This study presents a complete computational framework for replicating, validating, and extending RSM-based optimization of NiCo₂S₄–graphene supercapattery materials through automated Python implementation, demonstrating the feasibility of reproducible computational design methodologies.

We replicated the optimization study [1] using a 20-experiment CCD with a five-level three-factor design. Independent variables were graphene/NCS ratio (0.6–7.4%), hydrothermal time (4.6–11.4 hours), and S/Ni molar ratio (3.3–6.7). Specific capacitance served as the response variable. A quadratic polynomial model was developed using multiple linear regression, with automated analysis including ANOVA calculations, Pareto analysis (α = 0.05), Shapiro–Wilk normality testing, and response surface mapping implemented in Python.

The quadratic model achieved R² = 0.9716 and adjusted R² = 0.9460, explaining 97.16% of variance. The G/NCS ratio emerged as the dominant factor (57.19% contribution, p < 0.0001), with significant synergistic interactions for G/NCS×S/Ni (p = 0.0068) and time×S/Ni (p = 0.0057). Residuals demonstrated normal distribution (Shapiro–Wilk p = 0.8531). The optimal predictions were G/NCS = 6.0% and time = 10.0 h, S/Ni = 6.0, yielding 2263 F/g with 2.32% deviation from the original experimental results.

This automated Python framework validates RSM methodology through independent replication with 97.68% agreement with the original study [1]. The computational approach significantly reduces time investment while maintaining statistical validity, supporting industry adoption of data-driven design methodologies in energy storage materials. The complete Python code is provided as open-source supplementary material, enabling transparency, reproducibility, and broader methodological accessibility for future materials optimization research.

[1] Hong, Z.-Y. et al. Response Surface Methodology Optimization in High-Performance Solid-State Supercapattery Cells Using NiCo₂S₄–Graphene Hybrids. Molecules 2022, 27, 6867.

[2] Pandey, V.K. et al. A Response Surface Methodology Optimization Approach to Architect Low-Cost Activated Carbon-Based Ternary Composite for Supercapacitor Application with Enhanced Electrochemical Performance. Synth. Metals 2025, 311, 117844.

Currently, the large-scale integration of renewable energy sources (RESs) such as wind and solar photovoltaic generators is profoundly altering the dynamic behaviors of electrical grids, notably by reducing their inertia and making transient stability more critical. The originality of this work lies in its systematic exploration of the nonlinear dynamics of electrical networks, analyzing in depth the impact of RESs integration on grid stability and focusing on frequency response and rotor angle dynamics. This work proposes a methodology for evaluating and optimizing power system stability index, focusing on two major indicators: the Critical Clearing Time (CCT) and the Rate of Change of Frequency (ROCOF). The IEEE 39-bus test system is used as a test bench to simulate different renewable energy injection and microgrid (MG) location scenarios. Three-phase faults are applied to several grid lines to determine the corresponding CCTs and assess the system's ability to recover a stable state after disturbance. An automated approach was developed to calculate the CCT, enabling faster convergence and simultaneous testing of multiple fault locations. Variations in the ROCOF were also measured to quantify the effect of microgrids on frequency stability. The results show that high renewable energy penetration tends to reduce the CCT and increase the ROCOF, indicating a loss of dynamic robustness. However, optimizing the microgrid location to include an energy storage system (ESS) improves overall stability. These observations are supported by comparative simulations with and without renewable energy integration. Ultimately, this work highlights the importance of optimally locating distributed units and using CCT and ROCOF indices as diagnostic and optimization tools for modern power grids with high renewable energy penetration.

Maximum Power Point Tracking (MPPT) is essential to maximize energy extraction from photovoltaic (PV) arrays, especially in islanded microgrids, where grid support is absent and energy margins are tight. Traditional MPPT techniques and established metaheuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) have been applied widely, but they can suffer from slow convergence, premature trapping in local maxima under partial shading and variable irradiance, and suboptimal tradeoffs between tracking speed and steady-state oscillation. This paper systematically evaluates and compares a set of established and novel nature and bio-inspired metaheuristic optimizers for MPPT in an isolated microgrid context. First, PSO and GA are implemented as baseline methods under standard and partial-shading scenarios. Second, three recently proposed metaheuristics—Quokka Swarm Optimization (QSO), Dendritic Growth Optimization (DGO), and Brown-Bear Enhanced Optimization (EBOA)—are adapted to design an enhanced MPPT and tested for the first time in this application. The investigated algorithms are benchmarked in simulations against realistic PV models, dynamic irradiance/temperature profiles, and common partial-shading patterns. Performance metrics include convergence time to global MPP, energy capture over daily cycles, robustness to measurement noise, computational load, and susceptibility to false local maxima. Obtained results show that several of the newer optimizers achieve faster convergence and improved global MPP identification under challenging conditions while maintaining acceptable computational costs, suggesting promising alternatives to classical approaches for islanded microgrids. The paper concludes with implementation notes for embedded controllers and recommendations for future hardware validation.

The increasing complexity of modern manufacturing systems has amplified the need for intelligent energy monitoring solutions capable not only of visualizing consumption but also of detecting anomalies and predicting failures. Traditional monitoring applications provide real-time dashboards and performance indicators; however, they remain reactive and offer limited capacity for forecasting abnormal behaviors or guiding improvement initiatives. This research proposes an enhanced intelligent energy monitoring framework for smart factory environments, combining neural network modeling with a Six Sigma analytical approach to strengthen prediction accuracy and process stability. The study begins by examining the operational logic of conventional energy monitoring systems, highlighting their architecture, data flow mechanisms, and functional limitations. To address these gaps, a predictive model based on artificial neural networks is developed and trained using real production data to detect erroneous measurements, and predict potential failures related to equipment or data acquisition. Six Sigma methodology is integrated into the modeling process to structure the improvement cycle, refine parameter selection, evaluate statistical significance, and ensure robust model validation through performance metrics and process capability indices. The proposed solution is finalized through the design of a new application framework embedding the predictive model into the existing monitoring architecture. This intelligent platform enables continuous KPI tracking, automated alerts, and data-driven decision-making for proactive maintenance, process optimization, and energy efficiency. Results demonstrate that the integration of AI and Six Sigma improves monitoring intelligence, reduces reaction time, and strengthens operational reliability in Industry 4.0 environments. The findings confirm the potential of hybrid analytical approaches to transform energy monitoring from a descriptive system into a predictive and prescriptive decision-making tool.

The growing concern over greenhouse gas emissions has prompted the need for efficient carbon dioxide (CO₂) capture technologies. This study focuses on simulating CO₂ adsorption using a zinc-based metal-organic framework (Zn-MOF-5). The primary aim is to develop and refine a robust approach using MATLAB for equilibrium and kinetic modelling through the Linear Driving Force (LDF) model and Langmuir isotherm, which can accurately predict CO₂ adsorption performance under varying operational conditions. By employing advanced computational methods, this research seeks to streamline the process design and enhance the feasibility of sustainable CO₂ capture solutions. Excel was used for statistical analysis and validation, while MATLAB R2025a was utilised for equilibrium and kinetic modelling using the Linear Driving Force (LDF) model and the Langmuir isotherm. The independent impacts of temperature, pressure, and flow rate were evaluated using the variable effect method. This study found a significant negative association between temperature and CO₂ uptake, consistent with the exothermic nature of the adsorption process. Pressure had a considerable impact on adsorption, but flow rate had little effect within the investigated range. The simulated CO₂ uptake (21.196 mmol/g) closely matched the experimental data (21.07 mmol/g) with a 0.59% variance, validating the model's reliability. The research demonstrates that Zn-MOF-5 exhibits strong adsorption potential and that simulation tools can significantly reduce experimental costs and time. This underscores the potential of simulation tools to reduce costs and time, paving the way for more efficient carbon capture solutions. This initiative optimizes process design and promotes sustainable practices in addressing global CO₂ emissions. By contributing to process optimisation, this study aligns with the United Nations Sustainable Development Goal (SDG) 13, Climate Action, emphasizing the urgent need for innovative solutions to combat climate change and its impacts while supporting broader efforts for environmental sustainability.

The global energy transition promotes the development of microgrids integrating renewable sources such as wind energy. However, the intermittency of these sources creates challenges in terms of stability, synchronization, and optimal energy flow management. This research proposes an artificial intelligence-based approach to optimize control, synchronization, and energy sharing among distributed units within a microgrid connected to the national grid. The methodology relies on reinforcement learning algorithms and neural networks to dynamically adjust control parameters according to load conditions and generation fluctuations. Preliminary simulation results show a significant improvement in system stability and a reduction in energy losses. Furthermore, the proposed model can be adapted to different configurations of distributed generation systems, ensuring flexibility and scalability for real applications. Future work will focus on experimental validation through a laboratory-scale microgrid platform integrating real-time data acquisition and hardware-in-the-loop simulation. This step aims to confirm the robustness and adaptability of the proposed intelligent control under realistic operating conditions, paving the way toward autonomous and resilient smart energy systems. So it’s mandatory to take in consideration many external conditions which influence the stability of these microgrids .

These findings highlight the potential of intelligent control strategies for enhancing the integration of renewable sources in modern smart grids.

Introduction
Architectural envelope design is central to building energy performance, yet in practice it is often driven by heuristic rules and fragmented simulation workflows. This paper presents a machine learning-guided approach that translates high-resolution climate data into informed decisions on envelope geometry, material layering, and façade articulation, enabling architects to navigate complex interactions between solar gains, thermal inertia, daylight, and ventilation while preserving design freedom.

Methods
Typical Meteorological Year data from five Köppen climate classes (Cfa, Cfb, BWh, Dfa, Dfb) are processed into climate features (solar radiation clusters, diurnal temperature swings, humidity profiles, wind roses). These drive parametric envelope descriptors: orientation, window-to-wall ratio (15–60%), shading depth (0–1.5m), insulation thickness (50–300mm), and thermal mass distribution. A 12,000-variant dataset is generated using EnergyPlus and Radiance simulations. Gradient boosting and neural network models, validated via 5-fold cross-validation, predict energy use intensity (R²=0.91, RMSE=8.2 kWh/m²), overheating hours (R²=0.88), and daylight autonomy (R²=0.93) from combined climate–envelope feature vectors.

Results
Tested on office and mixed-use prototypes, the ML-guided workflow identifies envelope strategies reducing annual heating and cooling demand by 20–35% compared with ASHRAE 90.1-2019 code-minimum baselines (prescriptive envelope requirements, identical internal loads and schedules). SHAP-based sensitivity analyses on held-out data reveal climate-specific design drivers—shading geometry and solar control glazing in hot/arid contexts versus airtightness and insulation continuity in cold climates—providing interpretable, actionable guidance.

Conclusions
Machine learning can act as a climate-literate intermediary between raw weather data and envelope form-making. By embedding predictive models into parametric tools, architects gain rapid, intelligible feedback that elevates the building envelope from stylistic afterthought to a primary instrument of energy-efficient architectural design.

Introduction
Rapid urbanisation and intensifying climate variability are straining existing energy infrastructures, while city-level planning remains largely static and reactive. Urban digital twins—high-fidelity, data-rich virtual replicas of cities—combined with reinforcement learning (RL) offer new possibilities for adaptive, climate-responsive energy management. This paper explores how RL agents embedded in urban digital twins can orchestrate demand, storage, and distributed generation across buildings and districts to reduce energy use and emissions while preserving occupant comfort.

Methods
A multi-scale urban digital twin is constructed, integrating GIS-based morphology, building archetypes, district energy networks, and microclimate modelling. Real and synthetic data streams feed into the twin. RL agents (deep Q-learning and proximal policy optimisation, trained over 500 episodes with learning rate 3×10⁻⁴ and discount factor γ=0.99) control HVAC setpoints, shading devices, thermal storage, and battery dispatch at building and district levels. Reward functions encode multiple objectives, including minimising carbon intensity and peak demand while maintaining thermal comfort (operative temperature 20–26°C, PMV ±0.7). Comfort violations were deemed acceptable when affecting <5% of occupied hours. Scenario experiments explore different climate futures and urban design configurations.

Results
Simulation results for a mixed-use urban district show that RL-driven control reduces peak electrical demand by 18–30% and operational CO₂ emissions by 15–25% compared with rule-based schedules, while keeping comfort violations within acceptable limits. Coordinated control of façades, storage, and flexible loads mitigates urban heat island impacts during heatwaves. Policy analysis demonstrates how tariff design and incentive structures strongly influence RL convergence and system performance.

Conclusions
Coupling urban digital twins with reinforcement learning can transform static masterplans into adaptive, learning energy infrastructures. However, results are calibrated to a temperate European climate and mid-density mixed-use typology; generalisation to other climatic or morphological contexts requires further validation. Such tools nonetheless provide architects and policymakers with testbeds for prototyping climate-responsive districts.

Reliable open-source computational fluid dynamics (CFD) tools are essential for optimizing wind turbine aerodynamics and understanding complex wake interactions. This study presents a comparative analysis between Unsteady Reynolds-Averaged Navier–Stokes (URANS) and Large Eddy Simulation (LES) for predicting the turbulent wake of the NTNU T1 reference turbine. To address editorial requirements for high-fidelity quantitative validation, this research adopts the NTNU Blind Test 1 experimental benchmark Re»10^5.

The numerical framework, implemented in OpenFOAM, utilizes the Actuator Disc Model (ADM). To ensure physical consistency, the model explicitly incorporates the nacelle’s drag and employs experimental blade polars (S826) to accurately capture laminar separation bubble effects. Preliminary validation results demonstrate excellent agreement with experimental data, yielding a power coefficient CP of 0.45 and a thrust coefficient CT of 0.82.

Qualitative results contrast the ability of LES to resolve helical vortex structures and wake meandering against the numerical diffusion characteristic of the URANS approach. Quantitatively, the intercomparison focuses on velocity deficits and turbulence intensity recovery at 3D and 5D downstream locations. This study provides a validated workflow that balances physical fidelity and computational efficiency for offshore wind applications.

References

1- Krogstad, Per-Åge., Eriksen, P. E., & Melheim and J. A. (2011). “Blind test” workshop calculations for a model wind turbine (Technical report). Norwegian University of Science and Technology (NTNU), Department of Energy and Process Engineering.