The bibliometric analysis investigated the impact of publications on trends in the literature and bioenergy research using artificial intelligence (AI) from 1991 to 2021. In this study, 1721 publications were extracted from the Web of Science, and an analysis of the countries, authorship, institutions, journals, and keywords was visualised. In the recent decades, this field has entered an outbreak phase. India was the most productive country in this area, followed by China, Iran, and the US. It also noted several notable differences between trends and subjects in developed and developing countries. The former led this field at the initial stage and later attached importance to using AI for research feedstock and impact assessment. Developing countries encouraged the advancement of this area and emphasised the feedstock usage of phase treatment and process optimisation. In addition, a co-authorship and institutes study revealed that authors and institutes in distant regions rarely colla... [more]

In recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease with the increasing generation of renewable power. This is important for both current and future investors, as it can affect profitability. To address these concerns, a long-term analysis is proposed here to examine the influence of the future electricity mix on Iberian electricity prices in 2030. In this study, we employed artificial intelligence forecasting models that incorporated the main electricity price-driven components of MIBEL, providing accurate predictions for the real operation of the market. These can be extrapolated into the future to predict electricity prices in a scenario with high renewable power penetration. The results, obtained considering a framework featuring an increase in the penet... [more]

Unlike the more prevalent alternating current transmission systems, the high voltage direct current (HVDC) electric power transmission system transmits electric power using direct current. In order to investigate the precise remedy for fault detection of HVDC, this research proposes a method for the HVDC fault diagnostic methodologies with their limits and feature selection-based probabilistic generative model. The main contribution of this study is using the wavelet transform based on ant colony optimization and ANN to detect the different types of faults in HVDC transmission lines. In the proposed method, ANN uses optimum features obtained from the voltage, current, and their derivative signals. These features cannot be accurate to use in ANN because they cannot give reliable accuracy results. For this reason, first, the wavelet transform applies to the fault and non-fault signals to remove the noise. Then the ACO reduces unimportant features from the feature vector. Finally, the opt... [more]

With global warming, and internal combustion engine emissions as the main global non-industrial emissions, how to further optimize the power performance and emissions of internal combustion engines (ICEs) has become a top priority. Since the internal combustion engine is a complex nonlinear system, it is often difficult to optimize engine performance from a certain factor of the internal combustion engine, and the various parameters of the internal combustion engine are coupled with each other and affect each other. Moreover, traditional experimental methods including 3D simulation or bench testing are very time consuming or expensive, which largely affects the development of engines and the speed of product updates. Machine learning algorithms are currently receiving a lot of attention in various fields, including the internal combustion engine field. In this study, an artificial neural network (ANN) model was built to predict three types of indicators (power, emissions, and combustio... [more]

Spent grains from microbreweries are mostly formed by malting barley (or malt) and are suitable for a further valorization process. Transforming spent grains from waste to raw materials, for instance, in the production of nontraditional flour, requires a previous drying process. A natural convection solar dryer (NCSD) was evaluated as an alternative to a conventional electric convective dryer (CECD) for the dehydration process of local microbrewers’ spent grains. Two types of brewer’s spent grains (BSG; Golden ale and Red ale) were dried with both systems, and sustainability indices, specific energy consumption (eC), and CO2 emissions were calculated and used to assess the environmental advantages and disadvantages of the NCSD. Then, suitable models (empirical, neural networks, and computational fluid dynamics) were used to simulate both types of drying processes under different conditions. The drying times were 30−85 min (depending on the drying temperature, 363.15 K and 333.15 K) and... [more]

Pollution of the environment with heavy metals requires finding solutions to eliminate them from aqueous flows. The current trends aim at exploiting the advantages of the adsorption operation, by using some low-cost sorbents from agricultural waste biomass, and with good retention capacity of some heavy metal ions. In this context, it is important to provide tools that allow the modeling and optimization of the process, in order to transpose the process to a higher operating scale of the biosorption process. This paper capitalizes on the results of previous research on the biosorption of heavy metal ions, namely Pb(II), Cd(II), and Zn(II) on soybean biomass and soybean waste biomass resulting from biofuels extraction process. The data were processed by applying a methodology based on Artificial Neural Networks (ANNs) and evolutionary algorithms (EAs) capable of evolving ANN parameters. EAs are represented in this paper by the Differential Evolution (DE) algorithm, and a simultaneous tr... [more]

This study investigates chromium removal onto modified maghemite nanoparticles in batch experiments based on a central composite design. The effect of modified maghemite nanoparticles on the adsorptive removal of chromium was quantitatively elucidated by fitting the experimental data using artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modeling approaches. The ANN and ANFIS models, relating the inputs, i.e., pH, adsorbent dose, and initial chromium concentration to the output, i.e., chromium removal efficiency (RE), were developed by comparing the predicted value with that of the experimental values. The RE of chromium ranged from 49.58% to 92.72% under the influence of varying pH (i.e., 2.6−9.4) and adsorbent dose, i.e., 0.8 g/L to 9.2 g/L. The developed ANN model fits the experimental data exceptionally well with correlation coefficients of 1.000 and 0.997 for training and testing, respectively. In addition, the Pearson’s Chi-square measure (χ2)... [more]

Preparative chromatography is a well-established operation in chemical and biotechnology manufacturing. Chromatography achieves high separation performances, but often has to deal with the yield versus purity trade-off as the optimization criterium regarding through-put. The initial trade-off is often disturbed by the well-known phenomenon of chromatogram shifts over process lifetime, and has to be corrected by operators via adjustment of peak fraction cutting. Nevertheless, with regard to autonomous operation and batch to continuous processing modes, an advanced process control strategy is needed to identify and correct shifts from the optimal operation point automatically. Previous studies have already presented solutions for batch-to-batch variance and process control options with the aid of rigorous physico-chemical process modeling. These models can be implemented as distinct digital twins as well as statistical process operation data analyzers. In order to utilize such models for... [more]

Based on the context of Industry 4.0 smart manufacturing and for the prediction of injection molding quality of automobile bumpers, this study proposes a deep learning network that combines artificial neural networks and recognizable performance evaluation methods to better achieve the prediction and control of product quality. A pressure sensor was used to monitor and collect real-time pressure data in the mold cavity of the bumper. The quality indicators reflecting the molding quality were selected, and the correlation between these indicators and the molding quality was evaluated using recognizable performance evaluation methods and Pearson’s correlation coefficient. The standard z-score was used to filter out the abnormal data in the experimental data, and the bumper critical length warpage was converted into different quality levels, and the bumper critical length warpage was defined as either “qualified” and “unqualified” in order to improve the prediction accuracy of the model.... [more]

This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The framework includes microgrid with renewable energy sources (solar and wind), energy storage systems, electric vehicles (EVs), and building appliances like time flexible, power flexible, and base/critical appliances. For the proposed framework, we develop an ant colony optimization (ACO) algorithm, which efficiently schedules smart appliances, and EVs batteries charging/discharging with microgrid and without (W/O) microgrid under FPS to minimize energy cost, carbon emission, and peak to average ratio (PAR). An integrated technique of enhanced differential evolution (EDE) algorithm and artificial neural network (ANN) is devised to predict solar irradiance and wind speed for accurate microgrid energy estimation. To endorse the applicability of the proposed framework, simulations are conducted. Moreover, the pro... [more]

Voltage/frequency (V/f) drive of a three-phase induction motor plays a crucial role in a flour mill for energy saving. Wheat consumption in India is increasing day by day, which reached 105,000 metric ton (MT) in 2021. India’s high wheat consumption and production increase flour mills. Thus, energy efficiency in a flour mill is a must in the present situation. Hence, V/f drives are widely used in flour mills. Apart from the advantages of V/f drive, electronic circuits in a drive induce harmonics in a power system. Power quality plays a vital role in a modern power system. Harmonics by V/f drive increase the current consumption, causing increased losses, cable overheating, and motor overheating, which necessitates a filter for harmonic mitigation. In this paper, an artificial neural network controller-based hybrid power filter is proposed for harmonic mitigation. A hybrid power filter (HPF) is presented to overcome the problems and achieve the active and passive power filter’s benefits.... [more]

The P−U characteristic curve of the photovoltaic (PV) cell is a single peak curve with only one maximum power point (MPP). However, the fluctuation of the irradiance level and ambient temperature will cause the drift of MPP. In the maximum power point tracking (MPPT) algorithm of PV systems, BP neural network (BPNN) has an unstable learning rate and poor performance, while the genetic algorithm (GA) tends to fall into local optimum. Therefore, a novel PV fuzzy MPPT algorithm based on an adaptive genetic simulated annealing-optimized BP neural network (AGSA-BPNN-FLC) is proposed in this paper. First, the adaptive GA is adopted to generate the corresponding population and increase the population diversity. Second, the simulated annealing (SA) algorithm is applied to the parent and offspring with a higher fitness value to improve the convergence rate of GA, and the optimal weight threshold of BPNN are updated by GA and SA algorithm. Third, the optimized BPNN is employed to predict the MPP... [more]

With the application of complex fracturing and other complex technologies, external extrusion has become the main cause of casing damage, which makes non-API high-extrusion-resistant casing continuously used in unconventional oil and gas resources exploitation. Due to the strong sensitivity of string ovality, uneven wall thickness, residual stress, and other factors to high anti-collapse casing, the API formula has a big error in predicting the anti-collapse strength of high anti-collapse casing. Therefore, Bayesian regularization artificial neural network (BRANN) is used to predict the external collapse strength of high anti-collapse casing. By collecting full-scale physical data, including initial defect data, geometric size, mechanical parameters, etc., after data preprocessing, the casing collapse strength data set is established for model training and blind measurement. Under the classical three-layer neural network, the Bayesian regularization algorithm is used for training. Thro... [more]

When a liquid flows, it has an internal resistance to flow. Viscosity is the property that measures this resistance, which is a fundamental characteristic parameter of liquids. The monitoring of viscosity is essential for quality control in many industrial areas, such as the pharmaceutical, chemical, and energy-related industries. Several instruments measure the viscosity of a liquid, the most used being the capillary viscometers. These instruments are complex, associated with high cost and expensive prices. This represents a challenge in several industries, where accurate viscosity knowledge is essential in designing various industrial equipment and processes. Using image processing and machine learning algorithms is a promising alternative to the current measurement methods. This work aims to extract characteristic information from videos of droplets of different samples using image processing algorithms. An Artificial Neural Network model utilizes the extracted characteristics to cl... [more]

The performance of artificial neural networks (ANNs) is highly influenced by the selection of input variables and the architecture defined by hyper parameters such as the number of neurons in the hidden layer and connections between network variables. Although there are some black-box and trial and error based studies in the literature to deal with these issues, it is fair to state that a rigorous and systematic method providing global and unique solution is still missing. Accordingly, in this study, a mixed integer nonlinear programming (MINLP) formulation is proposed to detect the best features and connections among the neural network elements while propagating parameter and output uncertainties for regression problems. The objective of the formulation is to minimize the covariance of the estimated parameters while by (i) detecting the ideal number of neurons, (ii) synthesizing the connection configuration between those neurons, inputs and outputs, and (iii) selecting optimum input v... [more]

Yeasts are considered an extraordinary alternative source of natural carotenoids and pigmented terpenoids with multiple applications. Production of carotenoids by yeast fermentation technology has many benefits; it is cost-effective, easily scalable, and safe. The aim of this research is the isolation of yeasts from natural resources and selection of the most potent bioagent for carotenoid production. Additionally, an upgraded carotenoid extraction protocol we established, which implies the testing of four methods for cell lysis (hydrochloric acid treatment, ultrasound treatment, milling treatment, and osmotic pressure treatment), three extraction methods (conventional extraction, ultrasound extraction, and conventional + ultrasound extraction), and three extraction solvents (acetone, isopropanol/methanol (50:50), and ethanol). For the first time, the obtained results were further modeled by an artificial neural network (ANN). Based on the obtained maximal carotenoid yield (253.74 ± 9.... [more]

In this work, 2099 experimental data of binary systems composed of CO2 and ionic liquids are studied to predict solubility using a multilayer perceptron. The dataset includes 33 different types of ionic liquids over a wide range of temperatures, pressures, and solubilities. The main objective of this work is to propose a procedure for the prediction of CO2 solubility in ionic liquids by establishing four stages to determine the model parameters: (1) selection of the learning algorithm, (2) optimization of the first hidden layer, (3) optimization of the second hidden layer, and (4) selection of the input combination. In this study, a bound is set on the number of model parameters: the number of model parameters must be less than the amount of predicted data. Eight different learning algorithms with (4,m,n,1)-type hidden two-layer architectures (m = 2, 4, …, 10 and n = 2, 3, …, 10) are studied, and the artificial neural network is trained with three input combinations with three combinat... [more]

Both anthropogenic and natural sources of pollution are regionally significant. Therefore, in order to monitor and protect the quality of Langat River from deterioration, we use Artificial Intelligence (AI) to model the river water quality. This study has applied several machine learning models (two support vector machines (SVMs), six regression models, and artificial neural network (ANN)) to predict total suspended solids (TSS), total solids (TS), and dissolved solids (DS)) in Langat River, Malaysia. All of the models have been assessed using root mean square error (RMSE), mean square error (MSE) as well as the determination of coefficient (R2). Based on the model performance metrics, the ANN model outperformed all models, while the GPR and SVM models exhibited the characteristic of over-fitting. The remaining machine learning models exhibited fair to poor performances. Although there are a few researches conducted to predict TDS using ANN, however, there are less to no research condu... [more]

Sour water stripping can treat the sour water produced by crude oil processing, which has the effect of environmental protection, energy saving and emission reduction. This paper aims to reduce energy consumption of the unit by strengthening process parameter optimization. Firstly, the basic model is established by utilizing Aspen Plus, and the optimal model is determined by comparative analysis of back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) models. Then, the sensitivity analysis of Sobol is used to select the operating variables that have a significant influence on the energy consumption of the sour water stripping system. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the operating conditions of the sour water stripping unit. The results show that the RBFNN model is more accurate than other models. Its network structure is 5-66-1, and the expected value has an appro... [more]

Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the a... [more]

Optical density (OD) is a critical process parameter during fermentation, this being directly related to cell density, which provides valuable information regarding the state of the process. However, to measure OD, sampling of the fermentation broth is required. This is particularly challenging for high-throughput-microbioreactor (HT-MBR) systems, which require robotic liquid-handling (LiHa) systems for process control tasks, such as pH regulation or carbon feed additions. Bioreactor volume is limited and automated at-line sampling occupies the resources of LiHa systems; this affects their ability to carry out the aforementioned pipetting operations. Minimizing the number of physical OD measurements is therefore of significant interest. However, fewer measurements also result in less process information. This resource conflict has previously represented a challenge. We present an artificial neural-network-based soft sensor developed for the real-time estimation of the OD in an MBR syst... [more]

Manufacturing and energy sectors provide vast amounts of maintenance data and information which can be used proactively for performance monitoring and prognostic analysis which lead to improve maintenance planning and scheduling activities. This leads to reduced unplanned shutdowns, maintenance costs and any fatal events that could affect the operations of the overall system. Performance and condition monitoring are among the most used strategies for prognostic and health management (PHM), in which different methods and techniques can be implemented to analyse maintenance and online data. Offshore wind turbines (WTs) are complex systems increasingly needing maintenance. This study proposes a performance monitoring system to monitor the performance of the WT power generation process by exploiting artificial neural networks (ANN) composed of different network designs and training algorithms, using simulated supervisory control and data acquisition (SCADA) data. The performance monitoring... [more]

Brain tumours are considered one of the deadliest tumours in humans and have a low survival rate due to their heterogeneous nature. Several types of benign and malignant brain tumours need to be diagnosed early to administer appropriate treatment. Magnetic resonance (MR) images provide details of the brain’s internal structure, which allow radiologists and doctors to diagnose brain tumours. However, MR images contain complex details that require highly qualified experts and a long time to analyse. Artificial intelligence techniques solve these challenges. This paper presents four proposed systems, each with more than one technology. These techniques vary between machine, deep and hybrid learning. The first system comprises artificial neural network (ANN) and feedforward neural network (FFNN) algorithms based on the hybrid features between local binary pattern (LBP), grey-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. The second system comprises pre-t... [more]

The effective exploitation of smart technology in applications helps farmers make better decisions without increasing costs. Agricultural Research Centers (ARCs) are continually updating and producing new datasets from applied research, so the smart model should dynamically address all surrounding agricultural variables and improve its expertise from all available datasets. This research concentrates on sustainable agriculture using Adaptive Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANNs). Therefore, if a new related dataset is created, this new incoming dataset is merged with the existing dataset. The proposed PSO then bypasses the summarization of the dataset. It deletes the least essential and speculative records and keeps the records that are the most influential in the classification process. The summarized dataset is interposed in the training process without re-establishing the system again by modifying the classical ANN. The proposed ANN comprises an ada... [more]

There are many petrochemical industries that need adequate knowledge of multiphase flow phenomena inside pipes. In such industries, measuring the void fraction is considered to be a very challenging task. Thus, various techniques have been used for void fraction measurements. For determining more accurate multiphase flow measurements, this study employed dual non-intrusive techniques, gamma-ray and electrical capacitance sensors. The techniques using such sensors are considered non-intrusive as they do not cause any perturbation of the local structure of the phases’ flow. The first aim of this paper is to analyze both techniques separately for the void fraction data obtained from practical experiments. The second aim is to use both techniques’ data in a neural network model to analyze measurements more efficiently. Accordingly, a new system is configured to combine the two techniques’ data to obtain more precise results than they can individually. The simulations and analyzing procedur... [more]