Artificial neural network modelling of supercritical fluid CO2 extraction of polyunsaturated fatty acids from common carp (Cyprinus carpio L.) viscera

Common carp viscera, obtained from Tikveš Lake in Macedonia, was investigated as a possible source of polyunsaturated (PUFA) fatty acids. Supercritical fluid CO₂ extraction (SFE-CO₂) was employed for extraction of investigated bioactive components. The GC-FID analysis on the total extract obtained by supercritical fluid CO₂ extraction confirmed the assumption of presence of these bioactive components. A three layer artificial neural network was created for prediction and modelling of the extraction yield of polyunsaturated fatty acids from lyophilized viscera matrixes. Operating values of pressure, temperature, mass flow of CO₂ and extraction time were defined as input vectors to the ANN where PUFA extraction yield was considered as an output vector. Created ANN model provided adequate fitting of experimental data, with a correlation coefficient of 0.9968 for the entire data set. RSM-3D method was employed for mathematical modelling of the ANN output values as a function of operating variables and their interactions.     

Dynamic crude oil fouling prediction in industrial preheaters using optimized ANN based moving window technique

The objective of this paper is to develop and validate a reliable, efficient and robust artificial neural network (ANN) model for online monitoring and prediction of crude oil fouling behavior for industrial shell and tube heat exchangers. To explore the complex dynamics of fouling, a new modeling strategy based on moving-window neural network approach is proposed. The essential character of this modeling approach is online updating of the ANN model whenever a new data block is available, so that it can effectively capture the slowly changing of process dynamics. The results of these models have been compared with appropriate sets of experimental data. The mean relative errors (MRE) of training and prediction subsets were about 6.61% and 8.06%, respectively. Since the data extraction in the refinery was performed every 2 h, the modeling approach led to an MRE of about 8% for fouling rate prediction of the next 50 h.     

Optimization of supercritical carbon dioxide extraction of Passiflora seed oil

This study investigates extraction of Passiflora seed oil by using supercritical carbon dioxide. Artificial neural network (ANN) and response surface methodology (RSM) were applied for modeling and the prediction of the oil extraction yield. Moreover, process optimization were carried out by using both methods to predict the best operating conditions, which resulted in the maximum extraction yield of the Passiflora seed oil. The maximum extraction yield of Passiflora seed oil was estimated by ANN to be 26.55% under the operational conditions of temperature 56.5 °C, pressure 23.3 MPa, and the extraction time 3.72 h; whereas the optimum oil extraction yield was 25.76% applying the operational circumstances of temperature 55.9 °C, pressure 25.8 MPa, and the extraction time 3.95 h by RSM method. In addition, mean-squared-error (MSE) and relative error methods were utilized to compare the predicted values of the oil extraction yield obtained from both models with the experimental data. The results of the comparison reveal the superiority of ANN model compared to RSM model.     

Ternary gas permeation through synthesized pdms membranes: Experimental and CFD simulation basedon sorption‐dependent system using neural network model

In this study, a predictive model for the separation of gases via a polydimethylsiloxane (PDMS) membrane has been developed. This model takes into account the effects of gas composition and pressure at the membrane surfaces on the gas sorption and diffusion coefficients in the membrane. Computational fluid dynamics (CFD) modeling has been employed in order to predict the behavior of a gas mixture containing C₃H₈, CH₄, and H₂ at various operating conditions and three zones (upstream, downstream, and membrane body). Artificial neural network (ANN) modeling has been applied to predict sorption and diffusion coefficients of each component of the gas mixture in the membrane. A procedure of calculation has been applied to combine the CFD modeling and the ANN modeling in order to predict sorption, diffusion, and composition of each component at various sites of the membrane. The results determined using the developed prediction model have been found to be in agreement with those determined using experimental investigations with an average error of 10.21%. POLYM. ENG. SCI., 54:215–226, 2014. © 2013 Society of Plastics Engineers     

人工神经网络模拟超临界萃取米槁精油

为了更好地研究超临界CO2流体萃取米槁精油过程,文中对该过程萃取动力学进行了分析,建立了3层反向传导的人工神经网络模型。以MATLAB软件为平台,编制模型程序。以温度、压力、超临界CO2流体流率和原料平均粒径为输入,以收率为输出对网络进行训练。训练好的模型可用于萃取过程预测,与实验结果比较,预测平均误差小于5.0%。说明该模型可以对萃取过程进行模拟和预测。     

Modeling the Flux Decline during Protein Microfiltration: A Comparison between Feed-Forward Back Propagation and Radial Basis Function Neural Networks

Flux decline under various operating parameters in cross-flow microfiltration of BSA (bovine serum albumin) has been studied. A hydrophobic PES (polyethersulfone) membrane with an average pore diameter of 0.2 µm was used in all experiments. The experiments were carried out to investigate the effect of protein solution concentration and pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and membrane pore size on the flux decline trend and membrane rejection at constant trans-membrane pressure and ambient temperature. Subsequently, the experimental data, as a relatively large data set, have been subjected to a modeling study using both feed-forward back-propagation (BP) and radial basis function (RBF) artificial neural network (ANN) models. It is shown that through appropriate selection of parameters, it is possible to model the process accurately. Furthermore, it is concluded that the prediction capacity of RBFNN is superior to the BPNN, especially in the case of membrane rejection prediction.     

Solid oxide electrolysis cell 3D simulation using artificial neural network for cathodic process description

An artificial neural network (ANN) is used for modeling electrochemical process in a porous cathode of SOEC. The neural network has the following input parameters: the overvoltage, the hydrogen and steam composition at electrode/electrolyte interface. Data for training and validating the ANN simulator is extracted from a validated model. Once the model is identified, the ANN can be successfully used for simulating electrochemical behavior of a SOEC electrode. The analytical expression of the network has been implemented in a three-dimensional multiphysics model of SOEC serial repeat unit (SRU). The expression takes into account micro-scale effects in the macro-scale model with a minimum cost of computation time. Gas flow velocity, species concentrations, current density and temperature distributions through the SRU have been calculated. It has been shown that the ANN could be used in the macro-scale model giving coherent results.     

"人工神经网络"方法用于超临界流体萃取模拟

在15－30MPa和303－323K条件下，用超临界CO2流体萃取沙棘籽油，结果表明，最高沙棘油收率（30MPa,308K)可达到90％以上，对过程进行动力学模拟，建立了超临界萃取过程的人工神经网络（ANN）模型，以MATLAB软件为平台，编制了SFE－ANN模拟程序系统，采用3层BP网络结构，以压力，温度、萃取时间为输入，以萃取出油量为输出对网络进行训练，由此得到的网络可以对萃取速率和单位时间床高方向的萃取出油量进行准确的模拟和预测，与实验结果比较证明，训练样本集误差为0．2％，测试样本集误差为0．5％，模拟误差小于6％。     

Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs

Over the years, accurate prediction of dew-point pressure of gas condensate has been a vital importance in reservoir evaluation. Although various scientists and researchers have proposed correlations for this purpose since 1942, but most of these models fail to provide the desired accuracy in prediction of dew-point pressure. Therefore, further improvement is still needed. The objective of this study is to present an improved artificial neural network (ANN) method to predict dew-point pressures in gas condensate reservoirs. The model was developed and tested using a total set of 562 experimental data point from different gas condensate fluids covering a wide range of variables. After a series of optimization processes by monitoring the networks performance, the best network structure was selected. This study also presents a detailed comparison between the results predicted by this ANN model and those of other universal empirical correlations for estimation dew-point pressure. The results showed that the developed model outperforms all the existing methods and provides predictions in acceptable agreement with experimental data. Also it is shown that the improved ANN model is capable of simulating the actual physical trend of the dew-point pressure versus temperature between the cricondenbar and cricondenterm on the phase envelope. Finally, an outlier diagnosis was performed on the whole data set to detect the erroneous measurements from experimental data.     

Prediction of Timber Kiln Drying Rates by Neural Networks

The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.     

BP神经网络的发展及其在化学化工中的应用

人工神经网络（ANN）由于本身具有极强的非线性映射能力、容错性、自学习能力得到广泛的应用。基于反向传播算法（BP）的神经网络作为ANN重要组成部分,在涉及多种非线性因素建模时,相对于传统的反应机理建模显示出巨大的优势。虽然神经网络的发展几经繁荣与冷落,但目前在不同领域已经获得成功的应用。本文概述了BP神经网络的映射原理、缺点以及相应的改进方法,介绍其在催化剂设计、动力学模拟、理化特性估算、过程控制与优化、化学合成与反应性能预测的应用现状,展示了使用不同优化方法的改进模型在实验设计与优化方面取得的成果。最后指出未来BP神经网络的发展要进一步结合数据深度挖掘与机器学习等技术,为今后化学化工领域的研究提供强有力的工具。     

Prediction of Timber Kiln Drying Rates by Neural Networks

The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.     

MODELING,OPTIMIZATION, AND CONTROL OF REVERSE OSMOSIS WATER TREATMENT IN KAZEROON POWER PLANT USING NEURAL NETWORK

Neural network modeling and the back-propagation concept were utilized to develop data-driven models for predicting reverse osmosis (RO) plant performance and finding control strategies. Considering different commissioning times, the process of three RO plants was successfully modeled using an artificial neural network (ANN). Moreover, long-term forecasting of performance degradation was developed. Time (h), transmembrane pressure (TMP; bar), conductivity (µs/cm), and flow rate (m³/h) were utilized as ANN inputs. The effects of operating time and TMP on performance at mean values of feed conductivity and flow rate were investigated using three-dimensional figures. Genetic algorithm (GA) was employed to find optimum paths of TMP, feed flow rate, and control strategies during a specific period of time. The RO plant was monitored for 5000 h corresponding to the results generated by GA (optimum paths), and experimental results were compared to the prediction made by the model. The differences strongly implied the robustness of the ANN model.     

SOFT COMPUTING METHOD FOR PREDICTION OF CO2 CORROSION IN FLOW LINES BASED ON NEURAL NETWORK APPROACH

An important aspect of corrosion prediction for oil/gas wells and pipelines is to obtain a realistic estimate of the corrosion rate. Corrosion rate prediction involves developing a predictive model that utilizes commonly available operational parameters, existing lab/field data, and theoretical models to obtain realistic assessments of corrosion rates. This study presents a new model to predict corrosion rates by using artificial neural network (ANN) systems. The values of pH, velocity, temperature, and partial pressure of the CO₂ are input variables of the network and the rate of corrosion has been set as the network output. Among the 718 data sets, 503 of the data were implemented to find the best ANN structure, and 108 of the data that were not used in the development of the model were used to examine the reliability of this method. Statistical error analysis was used to evaluate the performance and the accuracy of the ANN system for predicting the rate of corrosion. It is shown that the predictions of this method are in acceptable agreement with experimental data, indicating the capability of the ANN for prediction of CO₂ corrosion rate in production flow lines.     

Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines

In the literature, very few correlations have been proposed for hold-up prediction in slurry pipelines. However, these correlations fail to predict hold-up over a wide range of conditions. Based on a databank of around 220 measurements collected from the open literature, a correlation for hold-up was derived using artificial neural network (ANN) modeling. The hold-up for slurry was found to be a function of nine parameters such as solids concentration, particle dia, slurry velocity, pressure drop and solid and liquid properties. Statistical analysis showed that the proposed correlation has an average absolute relative error (AARE) of 2.5% and a standard deviation of 3.0%. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved prediction of hold-up over a wide range of operating conditions, physical properties and pipe diameters. This correlation also predicts properly the trend of the effect of the operating and design parameters on hold-up.     

Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R~2) of lower than 0.2%, 1.05 × 10~(-7) and 0.9994, respectively.     

Using Artificial Neural Network to Predict the Pressure Drop in a Rotating Packed Bed

Although rotating beds are good equipments for intensified separations and multiphase reactions, but the fundamentals of its hydrodynamics are still unknown. In the wide range of operating conditions, the pressure drop across an irrigated bed is significantly lower than dry bed. In this regard, an approach based on artificial intelligence, that is, artificial neural network (ANN) has been proposed for prediction of the pressure drop across the rotating packed beds (RPB). The experimental data sets used as input data (280 data points) were divided into training and testing subsets. The training data set has been used to develop the ANN model while the testing data set was used to validate the performance of the trained ANN model. The results of the predicted pressure drop values with the experimental values show a good agreement between the prediction and experimental results regarding to some statistical parameters, for example (AARD% = 4.70, MSE = 2.0 × 10^?5 and R² = 0.9994). The designed ANN model can estimate the pressure drop in the countercurrent flow rotating packed bed with unexpected phenomena for higher pressure drop in dry bed than in wet bed. Also, the designed ANN model has been able to predict the pressure drop in a wet bed with the good accuracy with experimental.     

Improved Artificial Neural Network for Data Analysis and Property Prediction in Slag Glass–Ceramic

The development of slag glass–ceramics has environmental and commercial value. However, new types of these materials are usually developed using the "trial and error" method because of little understanding of the relationship between the composition, processing, microstructure, and properties. In this paper, artificial neural network (ANN) technology was applied to investigate the relationship between the composition content and the properties of slag glass–ceramic. The investigation showed that the ANN model had an outstanding learning ability and was effective in complex data analysis. If the data set reflects the relationship of the composition and property, the trained network will learn the relationship and then give relatively accurate and stable prediction. A new "virtual sample" technology has also been created which improves the prediction performance of the network by providing greater accuracy and reliability. With this virtual sample technology, the ANN model can establish the exact relationship from a small-size-data set, and gives accurate predictions. This improved ANN model is a powerful and reliable tool for data analysis and property prediction, and will facilitate the material design and development of slag glass–ceramics.     

Treatment of oily wastewater using low cost ceramic membrane: Comparative assessment of pore blocking and artificial neural network models

This work addresses the performance and modeling of the separation of oil-in-water (o/w) emulsions using low cost ceramic membrane that was prepared from inorganic precursors such as kaolin, quartz, feldspar, sodium carbonate, boric acid and sodium metasilicate. Synthetic o/w emulsions constituting 125 and 250 mg/L oil concentrations were subjected to microfiltration (MF) using this membrane in batch mode of operation with varying trans-membrane pressure differentials (ΔP) ranging from 68.95 to 275.8 kPa. The membrane exhibited 98.8% oil rejection efficiency and 5.36 × 10⁻⁶ m³/m² s permeate flux after 60 min of experimental run at 68.95 kPa trans-membrane pressure and 250 mg/L initial oil concentration. These experimental investigations confirmed the applicability of the prepared membrane in the treatment of o/w emulsions to yield permeate streams that can meet stricter environmental legislations (<10 mg/L). Subsequently, the experimental flux data has been subjected to modeling study using both conventional pore blocking models as well as back propagation-based multi-layer feed forward artificial neural network (ANN) model. Amongst several pore blocking models, the cake filtration model has been evaluated to be the best to represent the fouling phenomena. ANN has been found to perform better than the cake filtration model for the permeate flux prediction with marginally lower error values.