APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR CREATION OF "BLACK BOX" MODELS OF ENERGETIC MATERIALS COMBUSTION

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling.

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Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks

Electronics ◽

10.3390/electronics9111923 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1923

Author(s):

Eduardo G. Pardo ◽

Jaime Blanco-Linares ◽

David Velázquez ◽

Francisco Serradilla

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Steam Reforming ◽

Real Data ◽

Memetic Algorithms ◽

Black Box ◽

Optimization Techniques ◽

Ann Model ◽

The Real ◽

Artificial Neural

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.

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Application of artificial neural networks for flood forecasting.

Global NEST Journal ◽

10.30955/gnj.000305 ◽

2013 ◽

Vol 6 (3) ◽

pp. 205-211

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

High Precision ◽

Black Box ◽

Error Prediction ◽

Simplified Models ◽

Prediction Technique ◽

Ann Models ◽

Artificial Neural ◽

Consistent Response

In hydrology, as in a number of diverse fields, there has been an increasing use of Artificial Neural Networks (ANN) as black-box simplified models. This is mainly justified by their ability to model complex non-linear patterns; in addition they can self-adjust and produce a consistent response when ‘trained’ using observed outputs. This paper utilises various types of ANNs in an attempt to assess the relative performance of existing models. Ali Efenti, a subcatchment of the river Pinios (Greece), is examined and the results support the hypothesis that ANNs can produce qualitative forecasts. A 7-hour ahead forecast in particular proves to be of fairly high precision, especially when an error prediction technique is introduced to the ANN models.

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Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models

Advances and Applications in Deep Learning ◽

10.5772/intechopen.92172 ◽

2020 ◽

Author(s):

Evren Dağlarli

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Black Box ◽

Learning Models ◽

Learning Methods ◽

Data Set ◽

Box Models ◽

Explainable Artificial Intelligence ◽

Artificial Neural ◽

Black Box Models

The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.

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Practical perspective of artificial neural networks as a function approximator, not an almighty black box

2014 12th International Conference on Optical Internet 2014 (COIN) ◽

10.1109/coin.2014.6950561 ◽

2014 ◽

Author(s):

Tae-Hyung Kim

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Black Box ◽

Artificial Neural ◽

Function Approximator

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Artificial neural networks: from black-box to grey-box modelling

Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.1994.415324 ◽

2002 ◽

Author(s):

R. Summers ◽

R. Dybowski

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Black Box ◽

Artificial Neural

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Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks

Applied Soft Computing ◽

10.1016/j.asoc.2019.105990 ◽

2020 ◽

Vol 87 ◽

pp. 105990 ◽

Cited By ~ 1

Author(s):

Helon Vicente Hultmann Ayala ◽

Didace Habineza ◽

Micky Rakotondrabe ◽

Leandro dos Santos Coelho

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

System Identification ◽

Radial Basis Function ◽

Basis Function ◽

Black Box ◽

Coevolutionary Algorithms ◽

Radial Basis ◽

Artificial Neural

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Real-time daily flow forecasting using black-box models, diffusion processes, and neural networks

Canadian Journal of Civil Engineering ◽

10.1139/l00-016 ◽

2000 ◽

Vol 27 (4) ◽

pp. 671-682 ◽

Cited By ~ 8

Author(s):

N Lauzon ◽

J Rousselle ◽

S Birikundavyi ◽

H T Trung

Keyword(s):

Neural Network ◽

Neural Networks ◽

Diffusion Process ◽

Diffusion Processes ◽

Black Box ◽

Linear Type ◽

Modeling Approaches ◽

Box Models ◽

Almost All ◽

Black Box Models

The purpose of this study is to compare three modeling approaches used for the prediction of daily natural flows 1-7 days ahead. Linear black-box models, which have been commonly used for modeling flows, constitute the first approach. The second approach, a linear type in the context of our application, is less known in the water resources field and is identified by the term diffusion process. The third approach uses models called neural networks, which have gained interest in many fields. All these approaches were tested on 15 watersheds from the Saguenay - Lac-Saint-Jean hydrographic system, located in the province of Quebec, Canada. Because the watersheds possess different physical characteristics, the models were tested under several runoff conditions. In this article, the focus is on results; all approaches along with their conditions of use have been detailed elsewhere in the literature. The results obtained showed that neural networks constitute, for almost all the watersheds studied, the best approach to forecast daily natural flows. The more flexible structure of neural networks allows a best reproduction of complex runoff conditions. However, neural networks are more sensitive to outliers present in observed natural flow series, which are used as inputs in the three models tested. In practice, to model flows at specific periods of the year, it seems preferable to establish seasonal models. If a neural network has an inadequate structure for the period under consideration, then it may produce less convincing results than the other two modeling approaches tested in this study.Key words: forecasts, flows, black-box model, diffusion process, neural network.

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