Abstract
A key issue for improving the industrial efficiency in energy and material resources usage is to integrate the dynamics of the process and its scenario in the actual plant operation decision making. In this work, a combination of statistical techniques has been used to build an automatic modelling tool based on Neural Networks, that overruns the limitations of modelling techniques based on the theoretical knowledge of the process principles. As the overall system can be run simultaneously with the process, the tool can be used to continuously readjust its parameters and to follow evolutionary processes. The resulting model can be applied for process forecasting and control, resulting in improvements in the process performance. In the Neural Network field, a new method is introduced to test the results and a heuristic is proposed to stop the learning process when the best model has been found.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
