Accounts of Experiences in the Application of Artificial Neural Networks in Chemical Engineering

Himmelblau, D. M.

doi:10.1021/ie800076s

Cited by 178 publications

(113 citation statements)

References 24 publications

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“…Over fitting easily leads to the disturbance of the network and predictions often lie outside the range of considered variables in a multilayered network even if the input data are totally noise free [48].…”

Section: Training Of the Ann Networkmentioning

confidence: 99%

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

et al. 2017

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This research work concerns with the transesterification of castor oil with methanol to form biodiesel. As the free fatty acid content in castor oil is more than 1%, an acid catalyst namely, H 2 SO 4 has been used for esterification. The experimental conditions were determined using central composite design method and the experiments were conducted in a 2 L working volume fully controlled reactor. The input conditions namely, catalyst concentration, methanol to oil molar ratio and temperature were varied, and % fatty acid methyl ester (FAME) content was determined. Based upon the experimental data, an ANN model has been developed which is used to predict %FAME yield for a given set of input conditions. The experimental data and the data predicted by the ANN model have been used to estimate the rate constants of a kinetic model. The ANN model predicts the % FAME yield within ±8% deviation, and the developed kinetic model shows successfully the effect of methanol to oil molar ratio on % FAME yield at 60°C and 3% (v/v) catalyst loading.

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Section: Training Of the Ann Networkmentioning

confidence: 99%

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

et al. 2017

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“…The number of neurons (4) in the hidden layer found to this application was chosen by trial and error, as was suggested by Himmelblau [11]. Figure 9 shows the simulated estimations from the ANN, together with experimental points, for whole branches (a) and leaves (b) at the temperature of 60˚C, and for stems (c) at the temperature of 50˚C.…”

Section: Empirical Model Based On Annmentioning

confidence: 99%

“…their coefficients determined, they can provide a rapid response for a new input [11] [12]. This technique has been successfully applied in different applications, such as to estimate the higher heating values of biomass fuels [13] or to predict air quality parameters [14], its use in drying applications is still incipient.…”

Section: Introductionmentioning

confidence: 99%

A Neural Based Modeling Approach for Drying Kinetics Analysis of Mint Branches and Their Fractions (Leaves and Stems)

Rosanova¹,

Maia²,

Freire³

et al. 2017

ACES

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This work is aimed at investigating regular mint (Mentha × villosa) drying behavior and assessing how the heterogeneous composition of plants affects their drying kinetics. Drying kinetics and sorption isotherms were evaluated for whole branches and their fractions (leaves and stems). Stems and leaves were characterized by measurement of dimensions, apparent density and initial moisture content. The moisture sorption isotherms were obtained under temperatures of 30˚C, 40˚C and 50˚C for branches, stems and leaves and the data were fitted to the GAB model. Mint branches and their fractions were oven dried at temperatures from 40˚C to 70˚C and were obtained kinetic curves for each part. Water sorption patterns were similar for leaves and stems and the GAB model described well the sorption behavior of both materials. At a constant temperature, the drying rates were higher for leaves in comparison to stems and the differences increased as the temperature was raised. Therefore, depending on drying conditions, the moisture distribution in dried branches might be significantly different. Since the leaves constitute the major fraction in branches, the drying rates of branches were closer to those of leaves. The kinetic curves were fitted to a diffusion model based on an analytical solution of Fick's second diffusion law and to an empirical model based on artificial neural network (ANN). The results showed that the model based on the ANN predicted the drying kinetics of the different parts better than the diffusive model. A single network was built to describe the kinetic behavior of branches and fractions in the whole range of temperatures investigated. The diffusive model based on fitting effective diffusivity did not provide good predictions of moisture content, probably because neither the dependence of effective diffusivity on the moisture content nor the heterogenei- 155ty and shrinking of static beds were considered.

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“…However, chemical processes such as this batch crystallization process present the natured nonlinear dynamic behavior and multivariable interactions between variables that cause actually highly difficulty to obtain the accurate model. In this way, neural networks offer alternative nonlinear models for implementing MPC in such as systems [10][11][12][13]. The applications of neural networks for chemical process modeling and MPC have also been investigated for SISO systems and iterative multistep neural network predictions in MPC based control for MIMO chemical processes [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Kittisupakorn¹,

Somsong²,

Hussain³

et al. 2017

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Abstract. A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases.

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Accounts of Experiences in the Application of Artificial Neural Networks in Chemical Engineering

Cited by 178 publications

References 24 publications

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

A Neural Based Modeling Approach for Drying Kinetics Analysis of Mint Branches and Their Fractions (Leaves and Stems)

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

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