PVPF Tool: An Automated Web Application for Real-Time Photovoltaic Power Forecasting

In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimized neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records' time with respect to the current year. The machine learning system was pre-trained and optimized based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC

Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural Networks

In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.</jats:p

Nanodiamonds and natural deep eutectic solvents as potential carriers for lipase

This study investigates the use of nanodiamonds (ND) as a promising carrier for enzyme immobilization and compares the effectiveness of immobilized and native enzymes. Three different enzyme types were tested, of which Rhizopus niveus lipase (RNL) exhibited the highest relative activity, up to 350 %. Under optimized conditions (1 h, pH 7.0, 40 ◦C), the immobilized ND-RNL showed a maximum specific activity of 0.765 U mg− 1 , significantly higher than native RNL (0.505 U mg− 1 ). This study highlights a notable enhancement in immobilized lipase; furthermore, the enzyme can be recycled in the presence of a natural deep eutectic solvent (NADES), retaining 76 % of its initial activity. This aids in preserving the native conformation of the protein throughout the reusability process. A test on brine shrimp revealed that even at low concentrations, ND-RNL had minimal toxicity, indicating its low cytotoxicity. The in silico molecular dynamics simulations performed in this study offer valuable insights into the mechanism of interactions between RNL and ND, demonstrating that RNL immobilization onto NDs enhances its efficiency and stability. All told, these findings highlight the immense potential of ND-immobilized RNL as an excellent candidate for biological applications and showcase the promise of further research in this field

Encapsulated deep eutectic solvent for esterification of free fatty acid

A novel encapsulated deep eutectic solvent (DES) was introduced for biodiesel production via a two-step process. The DES was encapsulated in medical capsules and were used to reduce the free fatty acid (FFA) content of acidic crude palm oil (ACPO) to the minimum acceptable level (< 1%). The DES was synthesized from methyltriphenylphosphonium bromide (MTPB) and p-toluenesulfonic acid (PTSA). The effects pertaining to different operating conditions such as capsule dosage, reaction time, molar ratio, and reaction temperature were optimized. The FFA content of ACPO was reduced from existing 9.61% to less than 1% under optimum operating conditions. This indicated that encapsulated MTPB-DES performed high catalytic activity in FFA esterification reaction and showed considerable activity even after four consecutive recycling runs. The produced biodiesel after acid esterification and alkaline transesterification met the EN14214 international biodiesel standard specifications. To our best knowledge, this is the first study to introduce an acidic catalyst in capsule form. This method presents a new route for the safe storage of new materials to be used for biofuel production. Conductor-like screening model for real solvents (COSMO-RS) representation of the DES using σ-profile and σ-potential graphs indicated that MTPB and PTSA is a compatible combination due to the balanced presence and affinity towards hydrogen bond donor and hydrogen bond acceptor in each constituent

Temperature Effect on Performance of Different Solar Cell Technologies

One of the main parameters that affect the solar cell performance is cell temperature; the solar cell output decreases with the increase of temperature. Therefore, it is important to select the proper solar cell technology that performs better at a specified location considering its average temperatures. In addition, the solar cell performance is directly reflected on the overall economics of the project. This paper is proposed to evaluate the variations in the performance of different solar cell technologies related to temperature in Amman, Jordan. Field data of weather station and three PV systems (Poly-crystalline, Mono-crystalline and Thin-film) of identical design parameters collected from Test Field Project at Applied Science Private University, Shafa Badran, Amman, Jordan. These data were analysed in the following way: estimated specific energy yield (kWh/kWp) for the three different PV systems was calculated depending on the measured value of solar irradiance and technical specifications of the installed solar panels and inverters, then the actual energy yield at different temperatures over one year was compared with the estimated value, so the deviations could be determined and actual temperature coefficients for energy yield could be calculated, knowing that the three PV Systems have identical design parameters (tilt angle, azimuth angle, type and dimensions of mounting structure and inverter size) and same cleaning method and schedule. It was found that the thin-film solar panels are less affected by temperature with temperature coefficient of -0.0984%, and -0.109%, -0.124% for Mono-crystalline and Poly-crystalline respectively. These results can be implemented in the preliminary design steps, specifically in the selection of the solar cell technology to be installed in a specific location

Extreme Learning Machines for Solar Photovoltaic Power Predictions

The unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy sources becomes a fundamental issue for transmission system operators. As a result, forecasting techniques have obtained paramount importance. This work aims at exploiting the simplicity, fast computational and good generalization capability of Extreme Learning Machines (ELMs) in providing accurate 24 h-ahead solar photovoltaic (PV) power production predictions. The ELM architecture is firstly optimized, e.g., in terms of number of hidden neurons, and number of historical solar radiations and ambient temperatures (embedding dimension) required for training the ELM model, then it is used online to predict the solar PV power productions. The investigated ELM model is applied to a real case study of 264 kWp solar PV system installed on the roof of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. Results showed the capability of the ELM model in providing predictions that are slightly more accurate with negligible computational efforts compared to a Back Propagation Artificial Neural Network (BP-ANN) model, which is currently adopted by the PV system owners for the prediction task

Development of novel API-based deep eutectic solvents for esterification of high free fatty acid

Low-value feedstocks containing high free fatty acid (FFA) content are incompatible with direct alkali-catalysed transesterification, and require a deacidification step through esterification to reduce its FFA level. Herein, innovative acid catalysts were developed based on deep eutectic solvents (DESs) to pretreat low-quality palm oil (12.43 % FFA). DESs were formed using Brønsted acids (5-sulfosalicyclic acid and benzenesulfonic acid) and an active pharmaceutical ingredient (paracetamol) at a 3:1 M ratio. The DESs were characterized using ATR-FTIR and Hammett acidity function (H0). DES catalyst dosage, methanol requirement, reaction time and temperature parameters were optimized, and its recyclability was evaluated. The FFA contents were reduced to below the limit of &lt; 2 % using acidic DESs at optimized conditions. Reaction kinetics revealed that DES-catalysed reactions followed the pseudo first order rate of reaction and required the lowest activation energy of 40.91 kJ/mol. Through the Eyring-Polanyi thermodynamic study, the DES-catalysed esterification reactions were endothermic (ΔH° &gt; 0), non-spontaneous (ΔS° &lt; 0 and ΔG° &gt; 0) and endergonic. COSMO-RS computational calculations reveal the viable formation of the DESs based on its moieties, and supports the good solubility of the DESs in methanol. This study demonstrates the feasible valorisation of low-value feedstocks using innovative catalysts in enabling biodiesel production

Evaluation of biodegradability, toxicity and ecotoxicity of organic acid-based deep eutectic solvents

Over the past decade, deep eutectic systems (DES) have become popular, yet their potential toxicity to living organisms is not well understood. This study fills this gap by examining the toxicity, antibacterial activity and biodegradability of p-toluenesulfonic acid monohydrate (PTSA)-based DESs prepared from ammonium or phosphonium salts. Brine shrimp assays revealed varying toxicity levels of PTSA and salts. Allyltriphenylphosphonium bromide showing the longest survival time among all tested salts while PTSA exhibited a significantly longer duration of cell survival compared to other hydrogen bond donors. PTSA and ammonium salts (N,N-diethylethanolammonium chloride and choline chloride) as individual components showed non-toxic behavior for Gram-negative and Gram-positive bacteria while different PTSA-based DESs showed significant inhibition zones. Fish acute ecotoxicity tests indicated moderately toxicity for individual components and DESs, though higher concentrations increased fish mortality, highlighting the need for careful handling and disposal of PTSA-based DESs to the environment. Biodegradability analyses found all tested DESs to be readily biodegradable and it was reported that, DES 3 prepapred form PTSA and choline chloride has the highest biodegradability level. Notably, all tested DESs showed over 60 % biodegradability after 28 days. This groundbreaking study explores PTSA-based DESs, highlighting their biodegradability and potential use as antibacterial agents. Results revealed that PTSA as individual component is much better from toxicity point of view in comparison with PTSA-based DESs for any further industrial applications

Palm raceme as a promising biomass precursor for activated carbon to promote lipase activity with the aid of eutectic solvents

This study concerns the role of activated carbon (AC) from palm raceme as a support material for the enhancement of lipase-catalyzed reactions in an aqueous solution, with deep eutectic solvent (DES) as a co-solvent. The effects of carbonization temperature, impregnation ratio, and carbonization time on lipase activity were studied. The activities of Amano lipase from Burkholderia cepacia (AML) and lipase from the porcine pancreas (PPL) were used to investigate the optimum conditions for AC preparation. The results showed that AC has more interaction with PPL and effectively provides greater enzymatic activity compared with AML. The optimum treatment conditions of AC samples that yield the highest enzymatic activity were 0.5 (NaOH (g)/palm raceme (g)), 150 min, and a carbonization temperature of 400 °C. DES was prepared from alanine/sodium hydroxide and used with AC for the further enhancement of enzymatic activity. Kinetic studies demonstrated that the activity of PPL was enhanced with the immobilization of AC in a DES medium

Indole Derivatives Efficacy and Kinetics for Inhibiting Carbon Steel Corrosion in Sulfuric Acid Media

The global prevalence of metal corrosion is a significant challenge due to its detrimental effect. Environmentally friendly and non-hazardous alternatives for harmful and poisonous synthetic corrosion inhibitors are urgently necessary due to increasing environmental concerns and regulations prohibiting their application. In this study, indole molecules were employed as carbon steel corrosion inhibitors in acidic conditions. Gravimetrical and scanning electronic microscope (SEM) analysis was used in a preliminary investigation of indole as an organic inhibitor. The results revealed that adding indole to carbon steel before exposing it to sulfuric acid slowed and induced resistance to corrosion. The indole affixed themselves to the steel carbon surfaces, producing a barrier/protection for carbon steel. The efficacy of the indole in preventing corrosion was determined through the weight loss method. Temperature and inhibitory concentration effects on inhibition effectiveness under varying parameters were also reported. The temperatures employed were between 298 K and 328 K, while the inhibitor concentrations ranged from 1.2 × 10−3 M to 7.6 × 10−3 M, and both parameters significantly influenced corrosion inhibition effectiveness. The inhibitory mixture attained optimum efficacy in inhibiting corrosion, at 81 %, when the lowest and highest respective temperature and concentration were applied. The kinetic analysis was conducted under a range of temperatures to determine the reaction mechanisms of the inhibitor. The thermal adsorption isotherm of the inhibitor indicated that the surface adhered to Langmuir's adsorption isotherm. Additionally, investigations on corrosion and inhibition using the electrochemical impedance spectroscopy method (EIS) were conducted. This study can provide in-depth knowledge for advancing inhibitory science and engineering to enhance corrosion resistance in acidic media