Artificial intelligence in chemical engineering
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Artificial intelligence in chemical engineering
Interdisciplinary approaches to solve real world problems from the chemical engineering field
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On the Use of Artificial Neural Networks to Monitor a Pharmaceutical Freeze-Drying Process

On the Use of Artificial Neural Networks to Monitor a Pharmaceutical Freeze-Drying Process | Artificial intelligence in chemical engineering | Scoop.it
(2013). On the Use of Artificial Neural Networks to Monitor a Pharmaceutical Freeze-Drying Process. Drying Technology: Vol. 31, No. 1, pp. 72-81. doi: 10.1080/07373937.2012.718308
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This paper is focused on the use of artificial neural networks to monitor a pharmaceutical freeze-drying process. A detailed phenomenological model of the process is used to provide the dataset for the learning phase of the neural network development. Then, a methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the neural network which models the freeze-drying process. Using some experimentally available measurements at a generic time t, the neural network is able to estimate the temperature of the product and the thickness of the dried cake (the amount of residual ice, as well as the sublimation flux, can be easily calculated from the cake thickness) at a future time t + Δt, for the given operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). Also, the duration of the primary drying phase and the maximum product temperature in the future are predicted, in case the operating conditions are not modified. In this way it is possible to understand if it is necessary to modify the operating conditions, in case the product temperature should trespass the limit value before the ending point of the primary drying. Despite the fact that the artificial neural network is obtained using a learning set determined for specific values of heat transfer coefficient (between the heating shelf and the product at the bottom of the container) and of mass transfer resistance (of the dried cake to vapor flow), reliable and accurate estimations are also obtained in case the sensor is used to monitor a process characterized by different values of heat and mass transfer coefficients.

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ScienceDirect.com - Applied Soft Computing - Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermentation process

ScienceDirect.com - Applied Soft Computing - Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermentation process | Artificial intelligence in chemical engineering | Scoop.it

The determination of the optimal neural network topology is an important aspect when using neural models. Due to the lack of consistent rules, this is a difficult problem, which is solved in this paper using an evolutionary algorithm namely Differential Evolution. An improved, simple, and flexible self-adaptive variant of Differential Evolution algorithm is proposed and tested. The algorithm included two initialization strategies (normal distribution and normal distribution combined with the opposition based principle) and a modified mutation principle. Because the methodology contains new elements, a specific name has been assigned, SADE-NN-1. In order to determine the most influential inputs of the models, a sensitivity analysis was applied. The case study considered in this work refer to the oxygen mass transfer coefficient in stirred bioreactors in the presence of n-dodecane as oxygen vector. The oxygen transfer in the fermentation broths has a significant influence on the growth of cultivated microorganism, the accurate modeling of this process being an important problem that has to be solved in order to optimize the aerobic fermentation process.

The neural networks predicted the mass transfer coefficients with high accuracy, which indicates that the proposed methodology had a good performance. The same methodology, with a few modifications, and with the best neural network models, was used for determining the optimal conditions for which the mass transfer coefficient is maximized.

A short review of the differential evolution methodology is realized in the first part of this article, presenting the main characteristics and variants, with advantages and disadvantages, and fitting in the modifications proposed within the existing directions of research.

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ScienceDirect.com - Engineering Applications of Artificial Intelligence - Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolutio...

ScienceDirect.com - Engineering Applications of Artificial Intelligence - Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolutio... | Artificial intelligence in chemical engineering | Scoop.it

The search capabilities of the Differential Evolution (DE) algorithm – a global optimization technique – make it suitable for finding both the architecture and the best internal parameters of a neural network, usually determined by the training phase. In this paper, two variants of the DE algorithm (classical DE and self-adaptive mechanism) were used to obtain the best neural networks in two distinct cases: for prediction and classification problems. Oxygen mass transfer in stirred bioreactors is modeled with neural networks developed with the DE algorithm, based on the consideration that the oxygen constitutes one of the decisive factors of cultivated microorganism growth and can play an important role in the scale-up and economy of aerobic biosynthesis systems. The coefficient of mass transfer oxygen is related to the viscosity, superficial speed of air, specific power, and oxygen-vector volumetric fraction (being predicted as function of these parameters) using stacked neural networks. On the other hand, simple neural networks are designed with DE in order to classify the values of the mass transfer coefficient oxygen into different classes. Satisfactory results are obtained in both cases, proving that the neural network based modeling is an appropriate technique and the DE algorithm is able to lead to the near-optimal neural network topology.

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ResearchGate

After a neural network is trained. Can it really be validated?
Does it make sense to validate a NN after training?

 

 

Let A, B be training sets of n-vectors. Vectors in A are examples, or vectors with expected output 1. Vectors in B are counterexamples, with expected output 0. After training, a perceptron NN
P:R^n \rightarrow \{1,0\}
has outputs 1 on A, and 0 on B. So, it is 100\% accurate on the training data.

Recall that training is performed, in most cases, taking into account only the numerical values of the data vectors. Usually the training algorithms have no place for the 'meaning', 'interpretation', or 'empirical content', of the data.

Next, in order to 'validate' P, two sets of test data are given, A', B' and A'', B''.

Evaluating P on A', B' turns out to be 0\% accurate, that is, P(A')=0 and P(B')=1. The NN completely fails the first validating data set.

Finally, evaluating P on A'', B'' is 100\% accurate. Validation is completely successful in this case.

Many intermediate percentages of accuracy occur in practice.

All taken into account, the question arises: Was P well trained?

The criteria the neuroscientist used to choose both the training and the test vector sets ---in principle a subjective judgment--- must be taken into account. But the literature on NNs seldom carries an explicit discussion of these criteria.

Another way to put it: How to validate validation? This can be referred to as the problem of metavalidation.

References and opinions on this topic are welcomed.

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Amazon.com: Methods and Procedures for the Verification and Validation of Artificial Neural Networks eBook: Brian J. Taylor: Kindle Store

Amazon.com: Methods and Procedures for the Verification and Validation of Artificial Neural Networks eBook: Brian J.

 

This book presents neural network development from the perspective of the standards and requirements of  software development cycles.

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Modeling with Neural Networks: Principles and Model Design Methodology - Springer

Modeling with Neural Networks: Principles and Model Design Methodology - Springer | Artificial intelligence in chemical engineering | Scoop.it
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A verry good sinthetization related to artificial neural networks modeling: what they are, how they work

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Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks - Curteanu - 2011 - Journal of Chemometrics - Wiley Online Library

Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks - Curteanu - 2011 - Journal of Chemometrics - Wiley Online Library | Artificial intelligence in chemical engineering | Scoop.it

Artificial neural networks (ANNs) are comparatively straightforward to understand and use in the analysis of scientific data. However, this relative transparency may encourage their use in an uncritical, and therefore possibly unproductive, fashion. The geometry of a network is among the most crucial factors in the successful deployment of network tools; in this review, we cover methods that can be used to determine optimum or near-optimum geometries. These methods of determining neural network architecture include the following: (i) trial and error, in which architectures chosen semirandomly are tested and modified by the user; (ii) empirical or statistical methods, in which an ANN's internal parameters are adjusted based on the model's performance; (iii) hybrid methods, such as fuzzy inference; (iv) constructive and/or pruning algorithms, that add and/or remove neurons or weights from an initial architecture, respectively, based on a predefined link between architecture and ANN performance; (v) evolutionary strategies, which search the topology space using genetic operators to vary the neural network parameters. Several case studies illustrate the development of neural network models for applications in chemistry and chemical engineering.

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ScienceDirect.com - Chemical Engineering Science - Freeze-drying modeling and monitoring using a new neuro-evolutive technique

ScienceDirect.com - Chemical Engineering Science - Freeze-drying modeling and monitoring using a new neuro-evolutive technique | Artificial intelligence in chemical engineering | Scoop.it

Using the neural network model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determined off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization.

 

Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice).

 

Various examples are presented and discussed, thus pointing out the strength of the tool.

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ResearchGate

Neural Network for control system using Matlab

 


My question is can Feedforward Neural Network (FNN) be used in control system? In my opinion, the usage of Recurrent Neural Network (RNN) is more practical than FNN. But, the training using RNN takes a longer time than FNN and mostly during training, FNN will perform better than RNN. Yet, when we want to apply the FNN in the control system (such as in Direct Inverse scheme), many people used historical controller output as the input for the Neural Network controller. Thus, this make the FNN structure like RNN (refer attached figure). This is quite opposite to what we had train the NN using FNN scheme during training.

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