Artificial neural network based modelling and optimization of refined palm oil process

YesThe content and concentration of beta-carotene, tocopherol and free fatty acid is one of the important parameters that affect the quality of edible oil. In simulation based studies for refined palm oil process, three variables are usually used as input parameters which are feed flow rate (F), column temperature (T) and pressure (P). These parameters influence the output concentration of beta-carotene, tocopherol and free fatty acid. In this work, we develop 2 different ANN models; the first ANN model based on 3 inputs (F, T, P) and the second model based on 2 inputs (T and P). Artificial neural network (ANN) models are set up to describe the simulation. Feed forward back propagation neural networks are designed using different architecture in MATLAB toolbox. The effects of numbers for neurons and layers are examined. The correlation coefficient for this study is greater than 0.99; it is in good agreement during training and testing the models. Moreover, it is found that ANN can model the process accurately, and is able to predict the model outputs very close to those predicted by ASPEN HYSYS simulator for refined palm oil process. Optimization of the refined palm oil process is performed using ANN based model to maximize the concentration of beta-carotene and tocopherol at residue and free fatty acid at distillate

Quality prediction modeling of palm oil refining plant in Malaysia using artificial neural network models

Malaysia is currently one of the biggest producers and exporters of palm oil and palm oil products. In the growth of palm oil industry in Malaysia, quality of the refined oil is a major concern where off-specification products will be rejected thus causing a great loss in profit. In this paper, predictive modeling of refined palm oil quality in one palm oil refining plant in Malaysia is proposed for online quality monitoring purposes. The color of the crude oil, Free Fatty acid (FFA) content, bleaching earth dosage, citric acid dosage, activated carbon dosage, deodorizer pressure and deodorizer temperature were studied in this paper. The industrial palm oil refinery data were used as input and output to the Artificial Neural Network (ANN) model. Various trials were examined for training all three ANN models using number of nodes in the hidden layer varying from 10 to 25. All three models were trained and tested reasonably well to predict FFA content, red and yellow color quality of the refined palm oil efficiently with small error. Therefore, the models can be further implemented in palm oil refinery plant as online prediction system

Subcritical and supercritical fluid extraction a critical review of its analytical usefulness

Subcritical R134a is suggested as a low-pressure alternative to supercritical CO2 in the supercritical fluid extraction technology in particular of palm oil application. Therefore, a measurement of solubility of palm oil in subcritical Rl34a will be carried out at temperatures of 40, 60, 70 and 80°C and pressures up to 300 bar. The solubility of carotene are also will be measured using UV Spectrophotometer. Results obtained from this study will be compared with the previous work and for the first time, simulation for the SFE process of palm oil will be performed using Artificial Neural Network (ANN) and it will be implemented in comparisons as well when the operating conditions of the previous findings are different from this study. It is expected that the solubility of the palm oil in subcritical Rl34a is much higher than SC-C02, and it is expected that R134a could be a viable alternative solvent to supercritical carbon dioxide as R134a could be perform well at a lower pressure used whereas can achieved a higher solubility compared to SC-C0

Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning

Transportation and power generation have historically relied upon Internal Combustion Engines (ICEs). However, because of environmental impact and inefficiency, considerable research has been devoted to improving their performance. Alternative fuels are necessary because of environmental concerns and the depletion of non-renewable fuel stocks. Biodiesel has the potential to reduce emissions and improve sustainability when compared to diesel fuel. Several researchers have examined using nanofluids to increase biodiesel performance in internal combustion engines. Due to their thermal and physical properties, nanoparticles in a host fluid improve engine combustion and efficiency. This comprehensive review examines three key areas for improving ICE efficiency: biodiesel as an alternative fuel, application of nanofluids, and artificial intelligence (AI)/machine learning (ML) integration. The integration of AI/ML in nanoparticle-infused biodiesel offers exciting possibilities for optimizing production processes, enhancing fuel properties, and improving engine performance. This article first discusses, the benefits of biodiesel concerning the environment and various difficulties associated with its usage. The review then explores the effects and characteristics of nanofluids in IC engines, aiming to know their impact on engine emissions and performance. After that, this review discusses the utilization of AI/ML techniques in enhancing the biodiesel-nanofluid combustion process. This article sheds light on the ongoing efforts to make ICE technology more environmentally friendly and energy-efficient by examining current research and emerging patterns in these fields. Finally, the review presents the challenges and future perspectives of the field, paving the way for future research and improvement

Food science applications and international trends of artificial neural networks

Recently, research has been focusing increasingly on the system of artificial neural networks, and its results are used in many places by industrial practices. The success of these networks lies in their ability to recognize the complex relationships and patterns in data, as well as to predict unknown samples, thus enabling value and category predictions with high certainty. Artificial neural networks are very efficient tools for modeling non-linear trends within data. In many cases, they perform well where traditional statistical tools provide unsatisfactory results or unable to solve a given research problem. In our work, the operation principle and structure (topol-ogy) of artificial neural networks are summarized, as well as the classification and application possibilities of the networks. The latest food science applications are presented separately, based on the usage type (prediction, classification, optimiza-tion). Results show that artificial neural networks possess many beneficial properties, making them especially suitable for solving food science tasks

Utilización de redes neuronales para formular grasas técnicas

Neural networks are a branch of artificial intelligence based on the structure and development of biological systems, having as its main characteristic the ability to learn and generalize knowledge. They are used for solving complex problems for which traditional computing systems have a low efficiency. To date, applications have been proposed for different sectors and activities. In the area of fats and oils, the use of neural networks has focused mainly on two issues: the detection of adulteration and the development of fatty products. The formulation of fats for specific uses is the classic case of a complex problem where an expert or group of experts defines the proportions of each base, which, when mixed, provide the specifications for the desired product. Some conventional computer systems are currently available to assist the experts; however, these systems have some shortcomings. This article describes in detail a system for formulating fatty products, shortenings or special fats, from three or more components by using neural networks (MIX). All stages of development, including design, construction, training, evaluation, and operation of the network will be outlined.Las redes neuronales son una rama de la inteligencia artificial basadas en la estructura y funcionamiento de sistemas biológicos, teniendo como principal característica la capacidad de aprender y generalizar conocimiento. Estas son utilizadas en la resolución de problemas complejos, en los cuales los sistemas computacionales tradicionales presentan una eficiencia baja. Hasta la fecha, han sido propuestas aplicaciones para los más diversos sectores y actividades. En el área de grasas y aceites, la utilización de redes neuronales se ha concentrado principalmente en dos asuntos: la detección de adulteraciones y la formulación de productos grasos. La formulación de grasas para uso específico es el caso clásico de problema complejo donde un experto o grupo de expertos definen las proporciones de cada base, que al ser mezcladas proporcionaran características especificadas para el producto deseado. Algunos sistemas computacionales convencionales están disponibles actualmente para auxiliar a los expertos, sin embargo, estos sistemas presentan algunas deficiencias. En este artículo será descrito con detalles, un sistema para la formulación de productos grasos por redes neuronales (MIX) a partir de 3 o más componentes. Todas las etapas del desarrollo, incluyendo el diseño, construcción, entrenamiento, evaluación y operación de la red serán mostradas

Refined bleached deodorized palm oil quality prediction using multivariate statistical process control tools

Multivariate statistical process control (MSPC) has been widely used for quality prediction and monitoring in palm oil refinery processes. Currently, the refined, bleached deodorized palm oil (RBDPO) quality is determined based on the relationship between crude palm oil quality and process parameters, with the assumption that the process is static and not affected by the time-varying characteristic of the palm oil refinery process. However, the prediction is less accurate since the generated regression coefficients from static prediction models do not reflect the current process status and remain constant over time. Therefore, this study was conducted to introduce a new framework for regression coefficients improvement via dynamic prediction models. The dynamic prediction models were developed by integrating the MSPC prediction tool with time-series expansion methods where the prediction models were adapted to new process dynamics. Data collected from an industrial palm oil refining plant were used as the case study in this research. Four MSPC models, namely linear principal component regression (PCR), linear partial least squares (PLS), nonlinear principal component regression based on nonlinear iterative partial least squares algorithm (NIPALS-PCR) and nonlinear partial least squares based on nonlinear iterative partial least square algorithm (NIPALS-PLS) were used to determine the relationship between the quality and process variables. Time-series expansion methods were used to trace the dynamic behaviour based on five approaches, namely static, moving window (MW), recursive window (RW), exponentially weighted moving window (EWMW) and exponentially weighted recursive window (EWRW). The findings show that the combination of the linear prediction model with the time-series expansion method showed a more reliable prediction performance than the nonlinear prediction model. The performance of the PCR EWMW model in predicting the RBDPO quality is improved by 12.02 % (11.96 % for free fatty acid, 6.92 % for moisture content, 16.13 % for iodine value and 13.01 % for colour) compared to other prediction models. The sensitivity of the regression coefficients was also improved where the regression coefficients fluctuated very smoothly and showed high convergence to zero value when using the PCR EWMW model. This shows that the implementation of the linear dynamic prediction model was better than the static prediction model. Therefore, the linear dynamic prediction model for quality prediction was the best for it has the greatest prediction improvement and showed a better trend of the regression coefficient

AI-driven optimization of ethanol-powered internal combustion engines in alignment with multiple SDGs: A sustainable energy transition

With the escalating requirement for global sustainable energy solutions and the complexities linked with the complete transition to new technologies, internal combustion engines (ICEs) powered with biofuels like ethanol are gaining significance over time. However, problems linked to the performance and emissions of such ICEs necessitate accurate prediction and optimization. The study employed the integration of artificial neural networks (ANN) and multi-level historical design of response surface methodology (RSM) to address these challenges in alignment with the Sustainable Development Goals (SDGs). A single-cylinder spark ignition (SI) engine powered with ethanol-gasoline blends at different loads and speeds was used to gather data. Among six initially trained ANN models, the most efficient model with a regression coefficient (R2) of 0.9952 (training), 0.98579 (validation), 0.98847 (testing), and 0.99307 (overall) was employed to predict outputs such as brake power, brake specific fuel consumption (BSFC), brake thermal energy (BTE), concentration of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen NOx. Predicted outputs were optimized by incorporating RSM. On implementing optimized conditions, it was observed that BP and BTE increased by 19.9%, and 29.8%, respectively. Additionally, CO, and HC emissions experienced substantial reductions of 28.1%, and 40.6%, respectively. This research can help engine producers and researchers make refined decisions and achieve improved performance and emissions. The study directly supports SDG 7, SDG 9, SDG 12, SDG 13, and SGD 17, which call for achieving affordable, clean energy, sustainable industrialization, responsible consumption, and production, taking action on climate change, and partnership to advance the SDGs as a whole respectively

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Optimization of cerbera manghas biodiesel production using artificial neural networks integrated with ant colony optimization

© 2019 by the authors. Optimizing the process parameters of biodiesel production is the key to maximizing biodiesel yields. In this study, artificial neural network models integrated with ant colony optimization were developed to optimize the parameters of the two-step Cerbera manghas biodiesel production process: (1) esterification and (2) transesterification. The parameters of esterification and transesterification processes were optimized to minimize the acid value and maximize the C. manghas biodiesel yield, respectively. There was excellent agreement between the average experimental values and those predicted by the artificial neural network models, indicating their reliability. These models will be useful to predict the optimum process parameters, reducing the trial and error of conventional experimentation. The kinetic study was conducted to understand the mechanism of the transesterification process and, lastly, the model could measure the physicochemical properties of the C. manghas biodiesel