The global shift towards renewable energy has positioned solar photovoltaic (PV) systems as pivotal to achieving sustainable climate goals. However, the efficiency and performance of PV systems are significantly compromised by environmental factors, particularly dust accumulation, which can reduce energy output by up to 80% in arid and semi-arid regions. Despite the various PV cleaning technologies available, from manual methods to AI-driven robotic technologies that have emerged, their uneven maturity and scalability remain poorly quantified, delaying strategic deployment. This study presents the first systematic Technology Readiness Level (TRL) assessment of 12 PV cleaning methods, integrating technical performance, environmental impact, economic feasibility, and scalability. The findings reveal that while manual cleaning methods are widely deployed (TRL 9), they are labour-intensive and resource-dependent, making them unsuitable for large-scale applications in water-scarce regions. Emerging technologies, such as robotic cleaning (TRL 6-8), super-hydrophobic coatings (TRL 5-7), and electrodynamic screens (TRL 4-6), show high potential but require robust R&D to address durability, energy demands, and cost barriers. The study identifies key barriers to TRL advancement, including high initial costs, energy demands, durability issues, and environmental concerns, particularly for water-based systems. Conversely, enablers such as AI-driven predictive maintenance, hybrid approaches, and policy incentives offer pathways to accelerate the commercialization of sustainable cleaning solutions. By overcoming the challenges and making the most of the key drivers identified, this study aims to contribute to the global shift toward renewable energy, while helping to ensure that solar PV systems remain dependable and sustainable across a variety of environmental conditions.

Purpose This study aims to investigate the nonlinear coupled boundary-layer flow of a magnetohydrodynamic (MHD) non-Newtonian Eyring–Powell nanofluid over a porous cylinder using Physics-Informed Neural Networks (PINNs). Design/methodology/approach The governing nonlinear partial differential equations associated with momentum and heat transfer are transformed into a non-dimensionless form using appropriate transformations. The proposed PINNs framework incorporates governing physical laws directly into the learning process to achieve accurate and computationally efficient predictions of nonlinear heat transfer behavior. A deep PINN framework is then constructed in TensorFlow to solve the resulting equations while satisfying the imposed boundary conditions. The neural architecture consists of eight hidden layers with 156 neurons in each layer and is trained using a learning rate. The predictive performance of the developed PINN model is validated against the local non-similarity (LNS) method through comparisons. Findings The proposed PINN model demonstrates strong predictive accuracy and successfully captures the nonlinear transport behavior of MHD Eyring–Powell nanofluid flow. The numerical results exhibit excellent agreement with those obtained from the LNS method and established theoretical trends. The trained network accurately reproduces the thermal boundary conditions and heat transfer characteristics governed by the Prandtl number. Moreover, this study reveals that increasing the Eyring–Powell fluid material parameter significantly enhances the boundary layer thickness. This work also examines the effectiveness of PINNs in predicting heat transport characteristics and boundary layer behavior in comparison with the conventional LNS and finite difference method. This study confirming the robustness of the PINN framework as a reliable alternative for solving complex non-Newtonian transport phenomena in fluid flow and thermal engineering applications. Originality/value PINN-based modelling of MHD Eyring–Powell non-Newtonian boundary layer flow with heat transfer over horizontal cylindrical surfaces is still scarce in the literature.

Physics-informed neural network (PINN) is an excellent means to solve fluid dynamic and heat transfer equations (Partial Differential Equations (PDEs). They incorporate conservation laws directly into the loss function, where automatic differentiation ensures the solutions remain physically consistent even without a detailed computational mesh. The Adam optimizer is applied to compute an approximate solution with an accuracy of 10 to an accuracy of 10-3 with a learning rate of 0.001-0.0001. It is concerned with the flow of viscous incompressible fluid in the presence of mixed convection over a vertical stretching sheet. Thermal transport modeling considers the effects of viscous dissipation and Ohmic heating in conducting fluids caused by Lorentz forces. The governing flow equations are nonlinear, coupled partial differential equations that are reduced to dimensionless PDEs using non-similarity transformations. PINN is used to solve dimensionless equations of interest. Mean-absolute errors between the resulting velocity and temperature profiles is similar to benchmark results with the bvp4c solver in MATLAB. Graphical parametric analyses show that the velocity profile improves with increasing mixed convection parameter. In a low-conductivity regime, Joule heating is dominant, whereas viscous dissipation is the most important in a high-conductivity regime. Thermal performance improves with the addition of thermal radiation and nanoparticles to the system. Numerical values of the L2 error are provided to ensure the accuracy and validity of the developed scheme. These findings are relevant to engineering applications such as thermal regulation in polymer extrusion, MHD-enhanced cooling for microelectronics and photovoltaic panels, heat dissipation in fusion reactor blankets, and vehicle radiators. Mixed convection and dissipation effects critically impact efficiency and material longevity in these systems.

This study presents a comprehensive analysis of magnetohydrodynamic (MHD) squeezing flow of an incompressible fluid through a porous medium, incorporating the effects of viscous dissipation. Controlling heat and momentum transport in porous structures under magnetic influence is vital in industrial and energy-related processes. The flow configuration is driven by the normal motion of two parallel plates, with the upper plate approaching the stationary lower plate under the influence of an applied magnetic field. The governing nonlinear partial differential equations are derived using boundary layer approximations and then transformed and solved numerically. To predict skin friction and heat transfer rate, a statistical optimization framework is adopted using Response Surface Methodology (RSM). The effects of key parameters, including magnetic field strength, permeability of the porous medium, and Eckert number, are systematically investigated. Analysis of variance (ANOVA) is employed to assess the significance of individual and interaction effects of the physical parameters. The model exhibits strong predictive performance with a coefficient of determination (R2) of 98 % and an adjusted R2 of 96 % for drag reduction and R-sq 99.63 % R-sq(adj) 99.31 % for heat transfer rate, confirming its robustness and reliability. Additionally, a sensitivity analysis is conducted to identify the most influential parameters governing drag reduction and thermal performance. The findings provide valuable insights for optimizing heat and momentum transfer in engineering applications such as MHD generators, polymer extrusion, lubrication systems, and thermal management of porous structures.

Achieving a sustainable energy future requires efficient renewable conversion pathways, with biomass emerging as a promising alternative. Microwave-assisted pyrolysis (MAP) has attracted growing interest due to its high energy efficiency and product yields, yet its complex interplay of electromagnetic, thermal, and chemical processes demands advanced modelling for reliable design and optimisation. Despite the use of diverse software tools to study MAP, systematic evaluations of their capabilities remain scarce. This review critically assesses major modelling platforms used in MAP research, including process simulators (e.g., Aspen Plus, Aspen HYSYS), Computational fluid dynamics (CFD)-based solvers (e.g., COMSOL Multiphysics, ANSYS CFX, OpenFOAM), and statistical or machine learning environments (e.g., MATLAB, Design Expert). Their applications are compared in terms of feedstock dependence, operating conditions, and modelling features. Process simulators are particularly effective for flowsheet analysis and techno-economic studies, while CFD tools capture transport phenomena and reactor-scale behaviour with high resolution. Data-driven platforms complement these approaches by enabling optimisation and predictive analytics. Given the complexity of MAP, a modular modelling strategy is recommended, treating stages such as drying, heating, and pyrolysis independently with tailored methods. By consolidating existing knowledge and identifying gaps, this review provides a practical guide for researchers and engineers to select and integrate the most suitable numerical approaches for advancing MAP system development.

In the field of artificial intelligence and machine learning, physics-informed neural networks (PINNs) have received considerable attention because of their extensive applications in flow problems. PINN is a highly effective tool for discovering the intrinsic physics behind transport phenomena by incorporating governing equations into the training procedure of the neural network. The system of nonlinear partial differential equations is developed using non-Newtonian Casson fluid over a cylinder under the effect of a magnetic field in a porous medium. TensorFlow was employed to create and train the models, and the predicted results were compared with the reference solutions using the bvp4c method. This study compared the numerical and predicted solutions for parameter variation. The desired solutions were obtained by extending the parameter values, which required more neurons and hidden layers. To examine the prediction with PINNs, we used four number of hidden layer and three two number of neurons in the PINN design. In addition, the infinite boundary condition requires a suitable number of layers and neurons to be accounted for when the faraway boundary is set at a larger distance from the origin. The variations of various parameters are analyzed on flow output, i.e. velocity and temperature profiles. The interesting of Lorentz force is examined on fluid velocity and heat transfer analysis. It is noted Lorentz force have opposing effects on velocity. It is also noted that with the growing value of thermal radiation results in the increment of heat transfer.

Gyrotactic microorganisms, including unicellular algae and bacteria, exhibit directed movements in fluid flow. They form three-dimensional coherent structures in complex flows from mixing by commercial mixers. This study uses gyrotactic microorganisms as a new approach to chemical reaction systems, flow visualization and fluid mechanical measurements. The resulting planktonic fluid motion promotes mixing, and gyrotactic organism trajectories are used to visualize perpendicular flow components produced by different mixer configurations. The motions of sunset-colored bodies indicate complex fluid motion. Bioconvection is a phenomenon where microorganisms swim in response to gravity or light, affecting the boundary layer flow of stretching surfaces. In this work, a computational machine learning method is used to investigate the thermal performance of an EMHD non-Newtonian fluid on a porous stretching surface with Dufour-Soret effect of a hybrid nanofluid. MATLAB's built-in function bvp4c is used to create and transform the governing flow equations into appropriate ODEs. The Levenberg-Marquardt scheme with a Backpropagation Neural Network is the basis for supervised machine learning, which is used to calculate over a porous stretching surface of an approximated solution of a hybrid nanofluid of a non-Newtonian fluid. This study uses the MSE, correlation index, error histogram, and linear regression to validate the supervised machine learning approach. An optimal solution is obtained at epochs 344, 1000, 92, 195, 51, 56, and 126, with MSEs is 1.62 , 1.97 , 1.26 , 8.28 , 7.84 , 7.84 , and 7.97 , respectively. This investigation shows an increment in thermal, concentration, and microorganism boundary layer thickness against the raising value of pertained parameters, that is, thermal radiation, Lewis number and bio-convection.

Waste-to-energy technologies provide a sustainable solution to managing the increasing volume of restaurant food waste in Malaysia and Singapore while addressing environmental and economic challenges. This study conducted a comparative assessment of three Waste-to-energy technologies-anaerobic digestion, pyrolysis, and gasification-to evaluate their feasibility in converting food waste into renewable energy. The study also examined how policy and regulatory frameworks in Malaysia and Singapore influence the adoption of WtE solutions, offering actionable insights for the food industry and policymakers. The findings indicated that anaerobic digestion was the most promising technology, capable of reducing waste volume by 60 % and generating up to 5 MW of renewable energy per year of food waste processed. A techno-economic analysis (TEA) showed that anaerobic digestion was financially viable, providing a return on investment (ROI) of 12-15 % and a payback period of 5-7 years for medium-scale installations. The environmental impact assessment through life cycle analysis (LCA) revealed that anaerobic digestion reduced greenhouse gas (GHG) emissions by 0.8 kg CO2eq per kg of food waste compared to landfilling. With appropriate policies, technological advancements, and community engagement, Malaysia could generate an additional 75 MW of renewable energy from food waste, sufficient to power over 30,000 homes annually. These findings contribute to the transition toward a circular bioeconomy, supporting the United Nations Sustainable Development Goals (UN SDGs) and promoting a low-carbon, resource- efficient future for Malaysia and Singapore.

Homogeneous-heterogeneous reactions are essential in many fluid dynamics and engineering applications, particularly when system behavior is governed by both bulk and surface chemical processes occurring simultaneously. To maximize performance and efficiency, it is essential to comprehend and regulate these reactions in energy storage devices, combustion systems, environmental remediation technologies, and catalytic reactors. The author analyzes two-dimensional laminar flow of a fluid with homogeneous-heterogeneous reactions along a cylindrical surface by employing Artificial Neural Networks (ANN) and Machine Learning (ML). The governing partial differential equations are converted into nonlinear ordinary differential equations through similarity transformations, and the MATLAB bvp4c solver is applied to compute a numerical solution. To assess artificial intelligence-based models, such as LMS-BPNN and machine learning approaches that use a linear regression approach, the dataset produced by the bvp4c solver is divided into training, validation, and testing sets. A combination of fitness plots, error histograms, and mean squared error (MSE) analysis is used to evaluate the model's performance and accuracy. The findings show that the concentration boundary layer grows thicker when the Schmidt number increases. Table 4 illustrates the skin friction coefficient prediction with ANN and ML. Examines the advantages and uses of Artificial Neural Networks (ANN) and Machine Learning (ML) approaches in the study of non-Newtonian fluid mechanics for both scientific and engineering purposes. The results improve practical applications in fields including petrochemicals, renewable energy, medical technology, and aerospace engineering by optimizing fluid flow and reaction dynamics.

Artificial intelligence and machine learning revolutionizing the domain of fluid mechanic due to their precise modeling, optimization, and understanding the complex and nonlinearity more efficient. The author uses the AI-based Levenberg–Marquardt Scheme with a Backpropagation Neural Network (LMS-BPNN) to investigate the flow stability of MHD boundary layer flow of Casson Hybrid Nanofluid (CHNF) over a porous shrinking sheet. The partial differential equations (PDEs) that describe Casson hybrid nanofluid are transformed into a system of ordinary differential equations (ODEs) with efficient similarity variables. The initial/reference solution is generated using bvp4c function (an embedded MATLAB function designed to solve systems of ODEs) for various input parameters as demonstrated in scenarios 1–5. There are three options to divide numerical data: 80% for training, 10% for testing, and an additional 10% for validation. The LMS-BPNN is used to obtain the approximate solution for scenarios 1–5. The effectiveness and reliability of the proposed LMS-BPNN are validated through fitness curves based on correlation index ( R ), error, and regression analysis. It is noted that velocity and temperature profiles satisfy boundary conditions asymptotically for Senario1-5 with LMS-BPNN. Intelligent algorithms are used to calculate the dual solution for evaluating flow performance results. The perturbation scheme is applied to an unsteady boundary layer problem to obtain the eigenvalues problem. An unsteady solution f(η , τ ) converges to steady solution f_o(η ) for τ→∞ when γ≥ 0 . However, an unsteady solution f(η , τ ) diverges to a steady solution f_o(η ) for τ→∞ when γ <0 . It is found that the boundary layer thickness for the second (lower branch) solution is higher than the first (upper branch) solution. This investigation is the evidence that the first (upper branch) solution is stable and reliable. The analysis of errors demonstrates the consistency and reliability of the intelligent algorithm.

The primary concern to investigates boundary layer flow thermal and hydrodynamic conduct in battery systems using nanoparticles coupled with viscous dissipation and Ohmic heating effects under Cross fluid non-Newtonian flow circumstances. Machine Learning (ML) revolutionizes the study and applications of fluid mechanics and heat transfer analysis. Due to growing interest in machine learning and artificial neural network and focuses on computing the predicted solution of non-Newtonian nanofluid with Artificial Neural Network approximations. Nanoparticles addition boosts the system's thermal conductivity and energy efficiency, while energy dissipation and thermal fluctuations are governed by viscous effects and Ohmic heating. To attain optimal performance over a range of various conditions, it is important to understand the electrolyte's rheological behavior specifically its shear-thinning and shear-thickening features which is effectively captured by the Cross-fluid model. The governing equations undergo numerical solution to generate dataset which then trains ANN model to achieve high precision performance predictions for temperature distribution heat transfer rates besides energy efficiency. Hydrodynamic performance and heat management capabilities are greatly enhanced by a combined technique, and experimental testing is reduced thanks to the ANN model's successful deployment predictions regarding system dynamics. The findings of this study have the potential to improve thermal management in electric vehicles, pave the way for more efficient energy storage in grid-scale applications, portable electronics, aerospace batteries and renewable energy systems, and advance cooling systems for high-performance batteries. The aim of this initiative seeks to enhance the development of intelligent, sustainable energy storage systems for diverse real-world applications by integrating recurrent numerical simulations and artificial neural networks to elucidate methods for improving battery durability, safety, and efficiency.

Indonesia holds an estimated similar to 40 % of global geothermal resources, yet less than 8 % of this theoretical potential is operating. As the country seeks reliable, low-carbon power to meet ambitious renewable energy targets, geothermal can provide dispatchable generation that complements variable solar and wind. This review offers an integrated strategic assessment of Indonesia's geothermal sector by combining a literature-grounded, expert-elicited SWOT analysis with a project-level case study of the 70 MW Dieng development in Central Java. The expert scoring highlights substantial strengths (large, high-enthalpy resource base; proven operating experience; grid-supporting attributes) alongside material weaknesses (exploration risk and high upfront capital, protracted permitting, social-license frictions). Opportunities include national decarbonization commitments, access to climate finance, industrial heat applications, and grid expansion to resource-rich regions, while threats arise from tariff uncertainty, competing costs of other renewables and storage, and localized environmental and land-use pressures. Taken together, the weighted SWOT positions the sector in a Weaknesses-Threats (WT) posture, implying a defensive-stabilizing strategy that addresses risk and bankability before scaling. Actionable priorities include: (i) risk-sharing instruments for exploration (guarantees, drilling insurance, public co-funding); (ii) transparent, bankable PPA/tariff frameworks aligned with resource and drilling risk; (iii) coordinated transmission planning to connect priority prospects; (iv) standardized community-benefit and environmental management packages to strengthen social acceptance; and (v) modular technology choices (flash/binary hybrids) matched to resource grade. The Dieng case ground-truths these recommendations, illustrating how permitting, land access, and community engagement materially shape timelines and cost of capital. The synthesis provides a portable strategy set for policymakers, utilities, and developers to de-risk projects and accelerate geothermal deployment as part of Indonesia's sustainable energy transition.

The global freshwater crisis poses an existential threat to sustainable development worldwide. Desalination has emerged as a critical solution, but conventional fossil-fuel plants are energy-intensive and emit substantial greenhouse gases. Concentrating solar power (CSP) offers a promising renewable pathway to drive thermal desalination processes. However, CSP-desalination integration requires thoughtful system configuration design to maximize efficiency. This review consolidates insights from diverse case studies worldwide, highlighting the merits of CSP-desalination integration, such as significantly improved energy efficiency and sustainability through the utilization of renewable solar energy and enabling multi-generation systems for combined electricity, water, and heating services. The review's novelty lies in its systematic assessment of modeling simulations, pilot facilities, and commercial plants to elucidate key learnings on technical configurations and optimizations. It also proposes innovative configurations to enhance system efficiency and performance. The review identifies and analyzes optimization strategies employed in the reviewed case studies, including the role of thermal storage for 24-h operation, cogeneration for enhanced energy utilization, and multi-generation systems for combined electricity, water, and heating services. Recognizing the growing interest in hybrid systems, this review specifically examines the integration of thermal and membrane desalination processes driven by CSP, highlighting potential synergies and performance enhancements. The review provides a critical assessment of the diverse case demonstrations proving the technical viability of concentrated solar desalination under proper design conditions. It offers valuable insights on configurations that maximize renewable energy utilization and minimize water costs tailored to local ambient and operational parameters. Furthermore, it provides a forward-looking perspective by exploring the application of supercritical CO2 cycles in CSP-desalination systems, examining their potential for high-temperature heat supply without compromising power generation efficiency.

Artificial neural networks have reshaped machine learning by delivering unparalleled proficiency in unraveling intricate phenomena and addressing multifaceted challenges. Backpropagation remains the foundation for training these networks, but its optimization is essential for tackling sophisticated fluid dynamics problems. This study leverages the Levenberg–Marquardt technique integrated with artificial neural network backpropagation (LMT-BP-ANN) to examine the behavior of radially magnetized boundary layers in a unique nanofluid. Composed of nickel (Ni) , molybdenum disulfide (MoS_2) and tantalum (Ta) nanoparticles this nanofluid is investigated as it flows across a curved surface while managing heat dissipation and frictional forces with ethylene glycol serving as the base fluid. The fluid model is optimized by methodically varying critical physical parameters including the magnetic parameter, Reiner–Philippoff parameter, Bingham number, curvature parameter, Eckert number and thermal radiation parameter. The local non-similarity method implemented in MATLAB’s bvp4c solver generates the dataset for the LMT-BP-ANN framework. The neural network employs 70 10^-10 and an R-squared value of 1 during operation. The analysis further reveals that the fluid’s velocity diminishes as the magnetic parameter increases.

Palm oil industry generates a lot of waste, including empty fruit bunches (EFB), palm kernel shells (PKS), and palm oil mill effluent (POME), leading to environmental concerns. To become carbon-neutral by 2050, palm oil-producing countries are exploring the technological conversion of palm oil biomass (POB) into biofuels as an option to fossil fuels. This study introduces a novel approach to converting POB waste into sustainable biofuel, leveraging advanced catalytic processes and optimized reaction conditions. The innovative techniques achieve a conversion efficiency of over 85%, substantially higher than existing method, while reducing greenhouse gas (GHG) emissions by 60%. This dual impact on efficiency and environmental benefit underscores the transformative potential of our work. Moreover, our process utilizes 90% of POB waste, addressing waste management challenges. These breakthroughs position our research as a pivotal contribution to sustainable energy solutions and circular economy practices. Gasification of EFB and PKS generates high-quality syngas, which can be further processed into hydrogen fuel. Integrated biorefineries, combining two or more conversion methods, enhance overall efficiency, reduce management costs, and promote sustainability. The biorefineries can be optimized by integrating renewable resources such as solar energy, co-utilizing POBs, and synergizing with existing palm oil mill infrastructure. This integration supports the transition to a circular economy, achieving zero waste and reducing dependence on fossil fuels. However, POB commercialization has challenges such as optimized conditions for maximum yield, high cost, gas leakage, and social conflicts. The LCA of POB-based biofuels showed a reduction in EI compared to fossil fuels through anaerobic digestion and pyrolysis. Overall, this paper concludes with policy recommendations and regulatory frameworks essential to advance the commercialization of POB-based biofuels, highlighting the need for continued research and development to establish valorization systems.

The main purpose of the Levenberg–Marquardt Scheme to analyze flow behvaiour of on non-Newtonian fluid models influenced by gyrotactic microorganisms. The research addresses the complexities of fluid behavior in the presence of biological entities and thermal effects. The study begins by establishing the governing equations for fluid flow as partial differential equations, which are transformed into ordinary differential equations. The numerical solution are obtained with MATLAB ODEs solver bbvp4c. The Levenberg–Marquardt Scheme (LMS) is integrated with a Backpropagation Neural Network (BPNN) to enhance the accuracy of predictions. The efficacy of the proposed LMS-BPNN model is assessed using various statistical metrics, i.e. correlation index, linear regression, and mean squared error. These metrics confirm that LMS-BPNN model provides reliable predictions for fluid dynamics in this context. The study graphically illustrates the effects of various parameters on the momentum boundary layer, thermal boundary layer, species concentration, and motile microorganism behavior. The results show a strong correlation between numerical and predicted results, validating the effectiveness of the LMS-BPNN approach for modeling complex fluid behaviors involving non-Newtonian fluids and gyrotactic microorganisms. The thermal boundary layer grow as there is an increment in the values hybrid nanoparticles ( ϕ_1=ϕ_2 ). The study contributes valuable insights into how various parameters affect momentum, thermal dynamics, and concentration distributions in fluid mechanics, paving the way for future research in this domain.

The ability of nanofluids to carry nutrients and promote cellular connections makes them valuable for tissue engineering, pharmacokinetics, and stability enhancement in biomedical engineering. Energy transfer devices, cooling systems, and medication administration systems all benefit from their use. Additionally, they accelerate heat transfer. They also speed up the transfer of heat in manufacturing, polymer processing, and drug delivery, among other operations. Using the Levenberg–Marquardt scheme (LMS) algorithm, the work offers an AI-based approach to understanding stretching sheet behavior. Appropriate conversions are used to convert governing flow PDEs into ODEs. Scenarios 1–7 are given an initial reference solution created with MATLAB function ‘bvp4c’. Eighty percent is used for training, ten percent is for validation, and ten percent is for testing. Responses are estimated using LMS-BPNN in each of these scenarios. The effectiveness and reliability of the method are evaluated using regression analysis, correlation index, and error-based fitness curves. The study also examines flow performance indicators using LMS-BPNN to gain insights. The reliability and consistency of the proposed AI-driven technique are shown through error analysis.

Fossil fuels contribute heavily to global greenhouse gas emissions and their fast depletion necessitates sustainable alternatives. Biodiesel produced from non-edible oils like those of invasive plant species can provide renewable options without concerns of food security or land-use changes. This study experimentally investigates the combustion characteristics of biodiesel derived from Prosopis Juliflora (PJ), an invasive shrub in Ethiopia, when blended with diesel and diethyl ether (DEE) additive. The primary objective is to optimize the combustion of PJ biodiesel in a diesel engine by identifying the optimal DEE concentration and analyzing its impact on combustion parameters. Biodiesel was produced from PJ seeds through transesterification and blended in a 20% ratio with diesel fuel. DEE was added to this mixture in varying concentrations (5%, 10%, 15%, and 20%). The combustion characteristics, including cylinder pressure and heat release rate (HRR), were evaluated on a single-cylinder diesel engine at a constant speed of 2600 rpm under different engine loads. The results demonstrate that a 10% DEE blend yields the most significant improvement, with a 6.5% increase in peak cylinder pressure and a 4.7% rise in heat release rate, compared to baseline diesel. Furthermore, engine brake power output showed maximum enhancement at this DEE concentration. The improved combustion is attributed to the synergistic effects of PJ biodiesel's high cetane number and DEE's low viscosity and high volatility, which reduce ignition delay and enhance fuel atomization. This study provides the first comprehensive investigation into the combustion optimization of PJ biodiesel with DEE additives, demonstrating its potential as a sustainable, renewable alternative to petrodiesel without requiring engine modifications. Such non-edible biodiesel-ether blends provide feasible renewable substitutes to petrodiesel.