The digital twin (DT) technology implementation in photovoltaic (PV) systems provides an innovative approach to real-time performance monitoring and predictive maintenance. In this paper, an end-to-end DT framework for real-time performance analysis, fault detection, and optimization of a 250 W PV system is proposed. A physics-based equation and AI-based prediction hybrid DT model is developed through MATLAB/Simulink, trained from real data acquired by means of a testbed. The DT simulates the dynamic physical PV system behavior and adjusts itself using self-correcting algorithms to enhance precision in prediction and forecast power output at high fidelity. Results indicate that the DT gives the true response of the PV system with very small differences attributable to model approximations and sensor faults, 95% error minimization after compensation, and a root mean square error (RMSE) of 2.8 W, indicating its applicability for real-time monitoring and predictive main-maintenance. The work here focuses on the feasibility of applying DTs towards the autonomous optimization of distributed renewable energy systems.

There is an increasing shortage of water in dry and semiarid areas due to population growth, climate variability, and a lack of fresh water. Combining clean energy with desalination technology is an effective way to sustainably provide clean water in these areas, and this paper proposes a method for operating a renewable energy management system (EMS) with a combined wind turbine/battery‐powered reverse osmosis (RO) desalination system under these constraints. The RO will be modelled as a flexible electric load and water storage for buffering long‐term supply reliability. A detailed simulation of operations will be carried out using actual wind data from dry areas, similar to those in Kuwait and Iraq, under seasonal conditions. Two example scenarios will be studied: one for low‐wind (January) and another for high‐wind (July). The proposed EMS is evaluated against a conventional constant‐load baseline strategy using key performance indicators, including battery state‐of‐charge (SOC) stability, degradation index, wind energy utilisation, and freshwater production. Simulation studies have shown that the smart strategy method offers substantial improvements in a system's overall performance. For instance, in winter scenarios, this strategy reduces battery degradation by 49% while maintaining a comparable water volume from the facility. Similar improvements were observed during summer periods with high winds, with degradation reduced by 74%. The proposed strategy provided much greater stability in battery SOC variations and improved the operational lifespan of the batteries.

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis.

The lack of freshwater in the world requires a paradigm shift from linear water consumption to resilient and low-energy desalination technologies. Although reverse osmosis (RO) is the standard in the industry, its usefulness is essentially constrained by concentration polarization (CP) and non-useful hydraulic pressure losses. This paper applies a high-fidelity computational model in ANSYS Fluent 2022 R1 to conduct a comparative parametric evaluation of hexagonal and sinusoidal feed-spacer geometries relative to a baseline grid configuration. The solute concentration gradients at the fluid–membrane interface were solved using a 3D species transport model, which was optimized using one-micron near-wall inflation layers. The hexagonal configuration produced the lowest maximum membrane-surface salt mass fraction, decreasing it from 0.1127 kg/kg for the baseline grid to 0.0429 kg/kg, corresponding to a 61.9% reduction. Although the hexagonal design required an inlet pressure of 205.7 Pa, it produced a more favorable normalized mass-transfer–friction trade-off than the sinusoidal configuration (447.8 Pa), with a System Performance Index (η) of 2.53. These results demonstrate comparative micro-scale improvements in concentration polarization control and hydraulic performance under the simulated conditions. Experimental testing and system-level modeling are required before conclusions can be drawn regarding full-module energy consumption, photovoltaic integration, long-term fouling behavior, or economic feasibility. This study is consistent with the emerging Concepts and design for sustainability, whereby a circular and energy-efficient water economy is facilitated through an innovative mechanical design.

A numerical simulation of a direct contact membrane distillation (DCMD) cell with rectangular corrugations placed in the feed channel was conducted to investigate the influence of corrugated feed-channel geometry on hydrodynamic behavior and permeate flux in DCMD systems. The coupling of flow and mass transfer in conventional and modified channel geometries was simulated using a two-dimensional steady-state model. The effects of rib height, width, and pitch on pressure drop and permeate flux were systematically investigated. The findings indicated that rib geometry is a critical factor in determining masstransfer effectiveness and flow patterns. Specifically, increasing the rib height from 1 to 3 mm resulted in a significant reduction in permeate flux, mainly because of increased flow obstruction and the formation of concentration boundary layers. Similarly, permeate flux gradually decreased with increasing rib width. In contrast, increasing the rib pitch from 5 to 15 mm provided a small improvement in flux by reducing excessive flow blockage while maintaining satisfactory mixing. Pressure-drop analysis showed that smaller rib pitches increased hydraulic resistance, indicating a trade-off between enhanced mass transfer and increased flow losses. In general, the best configuration was achieved with a low rib height, intermediate width, and increased pitch, which balanced flux enhancement and minimum pressure loss. The results provide useful insight into the influence of rib geometry on masstransfer behavior and hydraulic performance in DCMD modules. The study demonstrates that geometric optimization of corrugated channels is necessary to balance flow disturbance and hydraulic resistance in DCMD systems.