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Many countries are planning their transition to renewable-energy-powered transportation sectors. Among those are developing countries, like Jordan, which face financial burdens in the actual implementation due to their economic status. As the penetration of electric vehicles increases, more green-charging stations will be required. Therefore, to encourage investors to initiate the deployment phase, this work presents a better mapping and comparison method for Photovoltaic (PV) battery charging stations across Jordan. It provides a better visual of the stations' techno-economic performance, so financiers can use it as a guide to building their future stations, given the economic returns presented, particularly since the designed algorithm considered profit-maximization of the highest priority. All the tested PV-battery configurations; namely, PV-Li-Ion, PV-Zn-Br, PV-Lead-acid, PV-Redox flow, and PV-Li-Ion second life, were feasible, with the southern parts of Jordan being more favorable for installation in terms of the techno-economic parameters. However, a PV-Zn-Br station was found to have the highest annual profit of $7,535 with 2.63 years return of investment and a renewable fraction of 96.7%. Finally, although the obtained results discussed the specifics of Jordan, this work can be extended to any other country planning its transition to a green transportation sector; to improve deployment by attracting investors. [ABSTRACT FROM AUTHOR]

The grid operators face challenges due to the fluctuations in the energy production from Photovoltaic (PV) power plants. Therefore, reliable prediction models are critical as they aid grid operators in optimizing operational planning and lowering operating costs. One of the most challenging components of production prediction accuracy is determining a proper set of features that affect the models' prediction accuracy. In this work, an accurate data-driven prediction model has been developed based on new timestamps and weather condition features that affect the PV system. Specifically, standard PV physics-based models have been employed for feature transformation and extraction from the traditional available historical features. The new features are then utilized to build an Artificial Neural Network (ANN), whose production predictions are compared to those obtained using the old features and other features extracted using the principal component analysis (PCA). The suggested approach's efficacy is demonstrated using a 264 kWp PV system in Jordan. The proposed approach has shown superior with performance gain in accuracy reaches up to 21% and 64% compared to the models that employ old and PCA features, respectively. Additionally, the proposed approach reduces the computational cost by 20% and 7% compared to old and PCA models, respectively. [ABSTRACT FROM AUTHOR]

Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and eco-friendly operations across multiple industries, notably in wastewater treatment plants (WWTPs). This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWh th. The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems. With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R 2 ) value of 68%. On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kW e capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES. Results indicate that the LCOE and PBP are highly affected by the ORC's capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. This study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field. • Analyzing the waste heat recovery (WHR) feasibility in wastewater treatment plants. • Developing data-driven approach-based artificial neural network for WHR estimation. • Economic analysis: Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage. • Electricity via WHR using ORC costs 0.0749 $/kWh, yielding 428 k$ yearly revenue. • Integrating seasonal thermal storage increases the levelized cost to 0.0824 $/kWh. [ABSTRACT FROM AUTHOR]

This paper proposes computational models to investigate the effects of dust and ambient temperature on the performance of a photovoltaic system built at the Hashemite University, Jordan. The system is connected on-grid with an azimuth angle of 0° and a tilt angle of 26°. The models have been developed employing optimized architectures of artificial neural network (ANN) and extreme learning machine (ELM) models to estimate conversion efficiency based on experimental data. The methodology of building the models is demonstrated and validated for its accuracy using different metrics. The effect of each parameter was found to be in agreement with the well-known relationship between each parameter and the predicted efficiency. It is found that the optimized ELM model predicts conversion efficiency with the best accuracy, yielding an R2 of 91.4%. Moreover, a recommendation for cleaning frequency of every two weeks is proposed. Finally, different scenarios of electricity tariffs with their sensitivity analyses are illustrated. [ABSTRACT FROM AUTHOR]

[Display omitted] • Comprehensive performance analysis & forecasting of a WWTP in Jordan is performed. • The plant's performance is analyzed by resorting to various statistical methods. • A data-driven approach to predict plant production 1-month in advance is proposed. • SVM emerges as top performer, showcasing potential of hybrid forecasting approach. • Such analysis offers valuable insights to enhance the plant's operability. This study presents a comprehensive performance and forecasting analysis of the As-Samra wastewater treatment plant (WWTP) in Jordan, with two main objectives. Firstly, a thorough evaluation of the plant's performance is conducted. The analysis involves independently assessing historical operational conditions, plant production, and their statistical correlations using various statistical techniques. The second objective focuses on developing a data-driven forecasting approach to predict the plant's production one month in advance, using multiple machine learning models. The results highlight the effectiveness of principal component analysis (PCA) in simplifying operational data, revealing distinct operational clusters, and identifying seasonal production patterns while showing correlations between operational conditions and overall power production. The support vector machine (SVM) forecasting model emerged as the top performer, showcasing the potential of a hybrid forecasting approach. The findings offer valuable perspectives for enhancing operational efficiency, refining production planning, and ultimately improving the environmental impact of the plant. [ABSTRACT FROM AUTHOR]

• LFG varies in species content and concertation based on the original waste material. • Optimizing the LFG combustion is required to maximize the landfill power output. • Real data from Al Ghabawi landfill was used as a model input. • A combustion model and ANN was used to optimize the landfill's operating parameters. • The intake pressure and the ignition timing were significant in the optimization. • Landfill's power output can be increased by around 1 MW for each genset. Landfills have high potency as renewable energy sources by producing biogas from organic waste degradation. Landfills biogas (LFG) can be used for power plant purposes instead of allowing it to flare to the atmosphere which contributes to the global warming. The aim of this work was to introduce and examine an optimization model for maximizing the power generation of Al Ghabawi landfill in Amman city, Jordan. The optimization process focused on studying the effect of several operating parameters within the landfill power plant. To achieve this goal, a combustion model had been built and validated against a set of historical real data obtained from the landfill operator. In addition to that, an Artificial Neural Network (ANN) model had been built to perform a multi-objective optimization to obtain the optimal power generation conditions for Al Ghabawi landfill. The combustion model along with the ANN model aim to estimate the best engine operating conditions based on the actual daily data of the landfill. The engine operating parameters includes the intake pressure and temperature, the ignition time and the equivalence ratio. The results of the study indicate that the current operating parameters can be optimized to maximize the gensets power generation. Based on the daily data of the produced LFG, the optimal operating conditions for the landfill are 2.32 bar for the intake pressure, 303 K for the intake temperature, 0.9–1.0 for the equiveillance ratio and for the ignition time it is 13 degrees before the top dead center (BTDC). These optimized operating parameters can maximize the landfill power generation by at least 1 MW for each genset. [ABSTRACT FROM AUTHOR]