Key Learnings from Twenty Years of Neural Network Applications in the Chemical Industry

Abstract : This talk summarizes several points that have been learned about applying Artificial Neural Networks in the chemical industry. Artificial Neural Networks are one of the major tools of Empirical Process Modeling, but not the only one. To properly assess the appropriate model complexity, combine information about both the Training and the Test data sets. A neural network, or any other empirical model, is better at making predictions than the comparison between modeled and observed data shows. Finally, it is important to exploit synergies with other disciplines and practitioners to stimulate the use of Neural Networks in industry.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.351-360, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_40〉
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Aaron Owens. Key Learnings from Twenty Years of Neural Network Applications in the Chemical Industry. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.351-360, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_40〉. 〈hal-01571356〉

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