Crystallization process optimization using artificial neural networks

This paper presents a new procedure for optimization of continuous mixed suspensionmixed product removal (MSMPR) crystallizing systems. Owing to the difficulties of theoretical modelling, simulation of the MSMPR crystallization process is based on the use of artificial neural networks (ANN). The optimization criterion is a compound objective function corresponding to an intended mean crystal size dimension and a minimal dispersion. The presence of multiple local minima has called for investigation by several optimization techniques. Ultimately, Luus' and Jaakola's random adaptive method proved to be most effective. The results obtained lend support to the general procedure proposed.     

Sintering of WC-Ni nanocomposite powder: Experimental and artificial neural networks modeling study

The sintering behavior of WC-Ni nanocomposite powder was evaluated through experimental and statistical approaches to study the contribution of involving parameters of chemical composition (Ni wt. %) and sintering temperature on sinterability of system by assessing the resulted densification and microhardness. The experimental process was designed based on factorial experimental design for independent effective parameters of Ni percentage (12, 18 and 23 wt %), and sintering temperature (8 different values within 1350–1485 °C). The resulted products of experimental testing after compaction and sintering were analyzed by FESEM and EDX to image the microstructure and evaluate the chemical composition and elemental distribution. The density and microhardness were measured as well. An artificial neural network (ANN) was applied to describe the corresponding individual and mutual impacts on sintering. The ANN model was developed by feed-forward back propagation network including topology 2:5:2 and trainlm algorithm to model and predict density and microhardness. A great agreement was observed between the predicted values by the ANN model and the experimental data for density and microhardness (regression coefficients (R²) of 0.9983 and 0.9924 for target functions of relative density and microhardness, respectively). Results showed that the relative importance of operating parameters on target functions (relative density and microhardness) was found to be 62% and 38% for sintering temperature and Ni percentage, respectively. Also, ANN model exhibited relatively high predictive ability and accuracy in describing nonlinear behavior of the sintering of WC-Ni nanocomposite powder. The experimental results confirmed that the appropriate sintering temperature was influenced by Ni content. The optimum parameters were found to be 12 wt % Ni sintered at 1460 °C with the highest microhardness and relative density.     

人工神经网络在化学工程中的应用

介绍了人工神经网络在化学工程中的应用，讨论了应用中的优点和局限性，预示了其应用的发展前景。     

The use of artificial neural networks in electrostatic force microscopy

ABSTRACT: The use of electrostatic force microscopy (EFM) to characterize and manipulate surfaces at the nanoscale usually faces the problem of dealing with systems where several parameters are not known. Artificial neural networks (ANNs) have demonstrated to be a very useful tool to tackle this type of problems. Here, we show that the use of ANNs allows us to quantitatively estimate magnitudes such as the dielectric constant of thin films. To improve thin film dielectric constant estimations in EFM, we first increase the accuracy of numerical simulations by replacing the standard minimization technique by a method based on ANN learning algorithms. Second, we use the improved numerical results to build a complete training set for a new ANN. The results obtained by the ANN suggest that accurate values for the thin film dielectric constant can only be estimated if the thin film thickness and sample dielectric constant are known.PACS: 07.79.Lh; 07.05.Mh; 61.46.Fg.     

神经网络在动力配煤中的应用

在动力配煤中考虑了混煤这个各煤参数之间的相关关系,建立动力配煤的数学模型,并提出神经网络算法,改进了一般的动力配煤模型。     

Discoloration process modeling by neural network

The photo-oxidation of acid orange 52 dye was performed in the presence of H₂O₂, utilizing UV light, aiming the discoloration process modeling and the process variable influence characterization. The discoloration process was modeled by the use of feedforward neural network. Each sample was characterized by five independent variables (dye concentration, pH, hydrogen peroxide volume, temperature and time of operation) and a dependent variable (absorbance). The neural model has also provided, through Garson Partition coefficients and the Pertubation method, the independent variable influence order determination. The results indicated that the time of operation was the predominant variable and reaction mean temperature was the lesser influent variable. The neural model obtained presented coefficients of correlation on the order 0.98, for sets of trainability, validation and testing, indicating the power of prediction of the model and its character of generalization.     

Applications of artificial neural networks in chemical engineering

A growing literature within the field of chemical engineering describing the use of artificial neural networks (ANN) has evolved for a diverse range of engineering applications such as fault detection, signal processing, process modeling, and control. Because ANN are nets of basis functions, they can provide good empirical models of complex nonlinear processes useful for a wide variety of purposes. This article describes certain types of neural networks that have proved to be effective in practical applications, mentions the advantages and disadvantages of using them, and presents four detailed chemical engineering applications. In the competitive field of modeling, ANN have secured a niche that now, after one decade, seems secure.     

Nonlinear system identification and model reduction using artificial neural networks

We present a technique for nonlinear system identification and model reduction using artificial neural networks (ANNs). The ANN is used to model plant input–output data, with the states of the model being represented by the outputs of an intermediate hidden layer of the ANN. Model reduction is achieved by applying a singular value decomposition (SVD)-based technique to the weight matrices of the ANN. The sequence of state values is used to convert the model to a form that is useful for state and parameter estimation. Examples of chemical systems (batch and continuous reactors and distillation columns) are presented to demonstrate the performance of the ANN-based system identification and model reduction technique.     

Context-based recognition of process states using neural networks

Patterns whose interpretation varies across contexts are common in many engineering domains. The resulting one-to-many mapping between patterns and their classes cannot be adequately handled by traditional pattern recognition approaches. This important class of pattern recognition problems although common has not received any attention in chemical engineering domain. In this paper, we show that identification of the state of chemical or biological processes is context dependent. Two types of features are important for context-based pattern recognition—primary features, which determine the class of a pattern, and contextual features, which cannot themselves predict the class, but can improve the effectiveness of the primary features. Process measurements can be used as primary features for identifying the current process state, and the previous process state provides the context in which the primary features have to be interpreted. We also propose a dynamic neural network architecture for context-based operating state identification. Three variations of the architecture, each using a different approach to identify change of context, are described. These are illustrated using two case studies for operating state identification—the startup of a simulated fluidized catalytic cracking unit and operation of a lab-scale fermentation process. A comparison with traditional neural networks reveals that the performance of the proposed context-based pattern recognition architecture is superior in all cases.     

Application of neural networks to meltblown process control

Process modeling is essential for the control of optimization and an on-line prediction is very useful for process monitoring and quality control. Up to now, no satisfactory methods have been found to model an industrial meltblown process since it is of highly dimensional and nonlinear complexity. In this article, back-propagation neural networks (BPNNs) were investigated for modeling the meltblown process and on-line predicting the product specifications such as fiber diameter and web thickness. The feasibility of this application was successfully demonstrated by agreement of the prediction results from the BPNN to the actual measurements of a practical case. The network inputs included extruder temperature, die temperature, melt flow rate, air temperature at die, air pressure at die, and die-to-collector distance (DCD). The output of the fiber diameter was obtained by neural computing. The network training was based on 160 sets of the training samples and the trained network was tested with 70 sets of test samples which were different from the training data. This research is preliminary and of industrial significance and especially valuable for the optimal control of advanced meltblown processes. © 1996 John Wiley & Sons, Inc.     

Predicting compressive strength of different geopolymers by artificial neural networks

In the present study, six different models based on artificial neural networks have been developed to predict the compressive strength of different types of geopolymers. The differences between the models were in the number of neurons in hidden layers and in the method of finalizing the models. Seven independent input parameters that cover the curing time, Ca(OH)₂ content, the amount of superplasticizer, NaOH concentration, mold type, geopolymer type and H₂O/Na₂O molar ratio were considered. For each set of these input variables, the compressive strength of geopolymers was obtained. A total number of 399 input-target pairs were collected from the literature, randomly divided into 279, 60 and 60 data and were trained, validated and tested, respectively. The best performance model was obtained through a network with two hidden layers and absolute fraction of variance of 0.9916, the absolute percentage error of 2.2102 and the root mean square error of 1.4867 in training phase. Additionally, the entire trained, validated and tested network showed a strong potential for predicting the compressive strength of geopolymers with a reasonable performance in the considered range.     

Modeling of strength of high-performance concrete using artificial neural networks

Several studies independently have shown that concrete strength development is determined not only by the water-to-cement ratio, but that it also is influenced by the content of other concrete ingredients. High-performance concrete is a highly complex material, which makes modeling its behavior a very difficult task. This paper is aimed at demonstrating the possibilities of adapting artificial neural networks (ANN) to predict the compressive strength of high-performance concrete. A set of trial batches of HPC was produced in the laboratory and demonstrated satisfactory experimental results. This study led to the following conclusions: 1) A strength model based on ANN is more accurate than a model based on regression analysis; and 2) It is convenient and easy to use ANN models for numerical experiments to review the effects of the proportions of each variable on the concrete mix.     

Hazard assessment of organic chemicals with the use of artificial neural networks

A procedure for assessing the hazard class of chemicals is developed based on the use of artificial neural networks. The structure of a neural network is founded on the structural analysis of chemical compounds. Training and testing were carried out within the computer database of chemical structures.     

Numerical modeling assessment of mechanical effect in bovine leather drying process

Leather manufacturing involves a crucial energy-intensive drying stage in the finishing process to remove its residual moisture and generates important heat gradients. The numerical model presented in this study has been developed to describe the drying process of porous medium: bovine leather that undergoes deformation due to shrinkage. The mathematical formulation of fundamental heat, mass and momentum transfers’ phenomena during drying summarizes a two-dimensional model considering elastic behavior of bovine leather. The evolution of moisture content, temperature, and mechanical stresses during drying was discussed. The model was validated with experimental results. Numerical simulations show good agreement with experimental results. The study shows that the elastic model keeps the stress sign at the final stage of drying. The deformations induce tensional stresses near the surface equilibrated by compressive stresses within the product. They reached their maximum for normal stresses equal to 5.97 and 3.52?MPa at around 2145 and 868?s, respectively, for normal stresses along x and y directions and then decrease.     

基于人工神经网络的过热蒸汽热流量计量系统

提出基于人工神经网络的过热蒸汽热流量测试技术,建立了基于人工神经网络模型的过热蒸汽质量和热流量的间接测量系统。在压力为0.28MPa—10.0MPa、温度为140℃—490℃范围,建立了过热蒸汽密度和密焓积的双输出人工神经网络模型,实现了热流量测量及质量流量的温度、压力补偿,并对模型的精度和外延性进行分析。模型输出密度的最大误差为2.047%,密焓积的最大误差为1.717%,具有较高的精度。     

Viscosity of ionic liquids using the concept of mass connectivity and artificial neural networks

Artificial neural networks (ANN) and the concept of mass connectivity index are used to correlate and predict the viscosity of ionic liquids. Different topologies of a multilayer feed forward artificial neural network were studied and the optimum architecture was determined. Viscosity data at several temperatures taken from the literature for 58 ionic liquids with 327 data points were used for training the network. To discriminate among the different substances, the molecular mass of the anion and of the cation, the mass connectivity index and the density at 298 K were considered as the independent variables. The capabilities of the designed network were tested by predicting viscosities for situations not considered during the training process (31 viscosity data for 26 ionic liquids). The results demonstrate that the chosen network and the variables considered allow estimating the viscosity of ionic liquids with acceptable accuracy for engineering calculations. The program codes and the necessary input files to calculate the viscosity for other ionic liquids are provided.     

乙醛生产过程数据挖掘

结合人工神经网络与遗传算法,对乙醛生产过程进行数据挖掘研究,从现有的生产数据中发现规律,实现了对乙醛生产过程模拟与优化。以乙醛收率为优化目标,建立人工网络模型并应用遗传算法对模型进行优化计算,获得生产过程最佳操作参数组合。实际应用表明,优化结果在高收率区。为化工生产过程优化提供一种新途径。