Moisture Content Prediction in the Switchgrass (Panicum virgatum) Drying Process Using Artificial Neural Networks

This article proposes two artificial neural network (ANN)-based models to characterize the switchgrass drying process: The first one models processes with constant air temperature and relative humidity and the second one models processes with variable air conditions and rainfall. The two ANN-based models proposed estimated the moisture content (MC) as a function of temperature, relative humidity, previous MC, time, and precipitation information. The first ANN-based model describes MC evolution data more accurately than six mathematical empirical equations typically proposed in the literature. The second ANN-based model estimated the MC with a correlation coefficient greater than 98.8%.     

Comparison of Estimation Capabilities of the Artificial Neural Network with the Wavelet Neural Network in Lipase-Catalyzed Synthesis of Triethanolamine-Based Esterquats Cationic Surfactant

In this study, estimation capabilities of the artificial neural network (ANN) and the wavelet neural network (WNN) based on genetic algorithm were investigated in a synthesis process. An enzymatic reaction catalyzed by Novozym 435 was selected as the model synthesis process. The conversion of enzymatic reaction was investigated as a response of five independent variables; enzyme amount, reaction time, reaction temperature, substrates molar ratio and agitation speed in conjunction with an experimental design. After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. Estimated responses were compared with the experimentally determined responses and prediction capabilities of ANN and WNN were determined. Performance assessment indicated that the WNN model possessed superior predictive ability than the ANN model, since a very close agreement between the experimental and the predicted values was obtained.     

神经元网络在化学工程中的应用与发展

综述了神经元网络在化工领域中的应用状况，并指出了今后的发展趋势。     

Application of Near-Infrared Spectroscopy for Evaluation of Drying Stress on Lumber Surface: A Comparison of Artificial Neural Networks and Partial Least Squares Regression

This study aimed to examine the feasibility of evaluating the stress level at the surface of lumber during drying using near-infrared (NIR) spectroscopy combined with artificial neural networks (ANNs). Sugi (Cryptomeria japonica D. Don) lumber with an initial moisture content ranging from 41.1 to 85.8% was dried using a commercial drying schedule. An ANN model for predicting surface-released strain (SRS) was developed based on NIR spectra collected from the lumber during drying. The predictive ability of the ANN model was compared with a partial least squares (PLS) regression model.

The ANN model showed good correlation between laboratory-measured SRS and predicted SRS with an R ² of 0.79, a root mean square error of prediction (RMSEP) of 0.0009, and a ratio of performance to deviation (RPD) of 1.81. The PLS regression model gave a lower R ² of 0.69, a higher RMSEP of 0.0010, and a lower RPD of 1.38 than the ANN model, suggesting that the predictive performance of the ANN model was superior to the PLS regression model. The SRS evolution during drying as predicted by the models showed a similar trend to the laboratory-measured one. The predicted elapsed times to reach maximum tensile SRS and stress reversal roughly coincided with the laboratory-measured times. These results suggest that NIR spectroscopy combined with multivariate analysis has the potential to predict the drying stress level on the lumber surface and the critical periods during drying, such as the points of maximum tensile stress and stress reversal.     

Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range.     

An Investigation of Drying Process of Shelled Pistachios in a Newly Designed Fixed Bed Dryer System by Using Artificial Neural Network

In this paper, the drying of Siirt pistachios (SSPs) in a newly designed fixed bed dryer system and the prediction of drying characteristics using artificial neural network (ANN) are presented. Drying characteristics of SSPs with initial moisture content (MC) of 42.3% dry basis (db) were studied at different air temperatures (60, 80, and 100 °C) and air velocities (0.065, 0.1, and 0.13 m/s) in a newly designed fixed bed dryer system. Obtained results of experiments were used for ANN modeling and compared with experimental data. Falling rate period was observed during all the drying experiments; constant rate period was not observed. Furthermore, in the presented study, the application of ANN for predicting the drying time (DT) for a good quality product (output parameter for ANN modeling) was investigated. In order to train the ANN, experimental measurements were used as training data and test data. The back propagation learning algorithm with two different variants, so-called Levenberg–Marguardt (LM) and scaled conjugate gradient (SCG), and tangent sigmoid transfer function were used in the network so that the best approach can be determined. The most suitable algorithm and neuron number in the hidden layer are found out as LM with 15 neurons. For this number level, after the training, it is found that Root-mean squared (RMS) value is 0.3692, and absolute fraction of variance (R²) value is 99.99%. It is concluded that ANNs can be used for prediction of drying SSPs as an accurate method in similar systems.     

Prediction of Bromate Removal in Drinking Water Using Artificial Neural Networks

In treatment of natural water resources, bromide transforms into carcinogenic bromate, especially during the ozonation process. Adsorption was used in the experimental part of this study to remove this harmful compound from drinking water. For this purpose, technically, HCl-, NaOH-, and NH₃-modified activated carbons were used. Scanning Electron Microscopy (SEM) and Brunauer–Emmett–Teller (BET) analyses were carried out within the characterization study. Moreover, the effects of diameters and heights of adsorption columns, flowrate, and particle size of adsorbent were investigated on the removal amounts of bromate. Optimum conditions were obtained from the experiments, and regional/real samples were collected and analyzed. After the experiments, an artificial neural network (ANN) was used to predict bromate removal percentage by using the observed data. Within this context, a feed-forward back-propagation ANN was chosen in this study. Additionally, the transfer function was selected as tangent sigmoid and 3 neurons were used in the hidden layer. Particle size and amount of the activated carbon, height and diameter of the column, volumetric flowrate, and initial concentration were selected as the input variables. Bromate removal percentage was selected as the output. It was found that the model an R value of 0.988, RMSE value of 3.47 and mean absolute percentage error (MAPE) of 5.19% in the test phase.     

利用ANN法预估芳香族多硝基化合物的密度

运用神经网络模型,采用误差反向传播算法,对一系列芳香族多硝基化合物的密度进行了预测.结果表明,芳香族多硝基化合物的密度与其分子结构存在良好的相关性,选用分子结构描述码作为输入特征参数能取得较高的预估精度,预测结果的相对误差一般在±10%以内.     

Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes

The aim of this study was to investigate the usefulness of combined application of quality by design tools such as central composite design (CCD), response surface methodology (RSM), and artificial neural networks (ANN) in the characterization, modeling, and optimizaton of spray drying of a poorly soluble drug : cyclodextrin complex. Models were developed by RSM and ANN from different pools of data. The model with best predictability was the ANN multilayer perceptron (MLP)1 model developed from the largest group of data (R ² for response yield 0.854, moisture content 0.886). On the other hand, analysis of equations derived from the application of RSM contributed in better understanding the complex relationships between input and output variables. By application of a desirability function approach, optimal process parameters that resulted in the best process yield (86%) and minimal moisture content in the powder (3.3%) were established (25% feed concentration, 180°C inlet air temperature, 10% pump speed).     

利用人工神经网络建立水滑石晶化过程的关联方程

采用人工神经网络方法对镁铝水滑石的连续晶化过程进行了研究,以便找出最佳的晶化条件。本文利用神经网络建立的模型,分析了成核料液的浓度、流量以及晶化温度、晶化时间对形成的水滑石晶体的影响,并由此建立了关联方程。研究表明最佳晶化条件:浓度在0.4~0.6 mol/mL,流量在18 mL/min左右,晶化时间400 min左右,晶化温度100℃。     

Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers

A spray dryer is the ideal equipment for the production of food powders because it can easily impart well-defined end product characteristics such as moisture content, particle size, porosity, and bulk density. Wall deposition of particles in spray dryers is a key processing problem and an understanding of wall deposition can guide the selection of operating conditions to minimize this problem. The stickiness of powders causes the deposition of particles on the wall. Operating parameters such as inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer, which together with the addition of drying aids can affect the stickiness and moisture content of the product and hence its deposition on the wall. In this article, an artificial neural network (ANN) method was used to model the effects of inlet air temperature, feed flow rate, and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products. An ANN trained by back-propagation algorithms was developed to predict two performance indices based on the three input variables. The results showed good agreement between predicted results using the ANN and the measured data taken under the same conditions. The optimum condition found by the ANN for minimum moisture content and minimum wall deposition rate for lactose-rich feed was inlet air temperature of 140°C, feed rate of 23 mL/min, and maltodextrin ratio of 45%. The ANN technology has been shown to be an excellent investigative and predictive tool for spray drying of lactose-rich products.     

纯物质汽化热的人工神经网络算法

１前言纯物质汽化热是重要的基础化工数据,其测定、关联、预测和理论研究相当活跃。在诸多预测模型中最有代表性的当属基团贡献模型,但多采用基团的简单加和或加权组合,基团贡献值由实验数据回归,通用性差,难以区别同分异构体,预测结果不尽人意。人工神经网络具有基...     

Theoretical Models of Single Droplet Drying Kinetics: A Review

During the last decades, growing attention has been given to theoretical and experimental studies of drying behavior of single droplet containing solids. This research interest is motivated by the need for fundamental understanding of the drying phenomena in extensively used technological processes like spray drying, fluidized bed drying, pneumatic drying, etc., at drop-wise and particulate levels. The present literature review summarizes the developed theoretical models of single droplet drying kinetics, discovers their benefits and deficiencies, and identifies prospects for future research.     

Drying Kinetics Prediction of Solid Waste Using Semi‐Empirical and Artificial Neural Network Models

The drying process of organic solid waste is investigated, based on an experimental study involving its drying kinetics. The experiments were conducted in a thin‐layer fixed‐bed dryer under various operational conditions. The problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well, using artificial neural network (ANN) models and four well‐known drying kinetics correlations commonly applied to biological materials. According to the statistical analysis employed, the simulations showed good results for the ANN, and the Overhults model provided optimum agreement with experimental data among all other models evaluated. Empirical correlations between the Overhults model parameters and the drying operational conditions using nonlinear regression techniques were determined.     

人工神经网络用于枞酸乙二醇单酯与双酯合成的分析

使用人工神经网络算法对枞酸乙二醇单酯与双酯合成反应正交试验数据进行辅助分析,探讨了反应温度、催化剂用量、反应物料比、反应时间对反应转化率及选择性的影响。结果表明:反应温度是关系酯化反应选择性的决定因素,反应物料配比是次要因素,投料比可以向目的物的构成比例方向优化,并可减少催化剂的用量。     

人工神经网络在材料科学领域的应用

在分析神经网络基本理论的基础上,通过列举神经网络在高分子、金属、合金等材料研究中的应用,分析讨论了其在材料领域中的研究进展情况和存在的问题,展望了人工神经网络在该领域中的发展前景.     

Comparison between two types of Artificial Neural Networks used for validation of pharmaceutical processes

Two types of Artificial Neural Networks (ANNs), a Multi-Layer Perceptron (MLP) and a Generalized Regression Neural Network (GRNN), have been used for the validation of a fluid bed granulation process. The training capacity and the accuracy of these two types of networks were compared. The variations of the ratio of binder solution to feed material, product bed temperature, atomizing air pressure, binder spray rate, air velocity and batch size were taken as input variables for training the MLP and GRNN. The properties of size, size distribution, flow rate, angle of repose and Hausner's ratio of granules produced, were measured and used as output variables. Qualitatively, the two networks gave comparable results, as both pointed out the importance of the binder spray rate and the atomizing air pressure to the granulation process. However, the averaged absolute error of the MLP was higher than the averaged absolute error of the GRNN. Furthermore, the correlation coefficients between the experimentally determined and the calculated output values, the corresponding prediction accuracy for the different granule properties as well as the overall prediction accuracy using GRNN were better than using MLP. In conclusion, the comparison of two different networks (MLP, a so-called feed-forward back-propagation network and GRNN, a so-called Bayesian Neural Network) showed the higher capacity of the latter for validation of such granulation processes.     

Computer Vision System (CVS) for In-Line Monitoring of Visual Texture Kinetics During Shrimp (Penaeus Spp.) Drying

In this study, the effects of drying medium temperature and velocity were surveyed on the image texture features of shrimp (Penaeus spp.) batches in a dryer equipped with a perpendicular dual-view computer vision system (CVS). This was carried out by applying an innovative rotation- and scale-invariant image texture processing approach with the capability of eliminating the effects of sample shrinkage on the visual textural features. Moreover, the variations in image texture parameters were investigated with moisture ratio, color, and geometrical characteristics of the shrimp samples. Drying experiments were conducted at hot air drying (HAD) temperatures of 50–90°C and superheated steam drying (SSD) temperatures of 110–120°C with drying medium velocities of 1–2 m/s. Several configurations of a multilayer perceptron artificial neural network (MLP-ANN) were also used to predict the moisture ratio and the geometrical characteristics of the shrimp batch using the image texture parameters. Generally, the image texture features were significantly affected by drying medium temperatures (p < 0.01), and the effects of drying medium velocities on the textural properties were nonsignificant (p > 0.05). Additionally, the higher drying temperatures generated products with uniform and regular texture patterns. The SSD produced samples with somewhat nonuniform and irregular texture patterns compared with HAD at 90°C. Finally, selected MLP-ANN topologies successfully predicted the moisture ratio and the geometrical characteristics of the shrimp batch using the textural properties with correlation coefficients higher than 0.99.