title: Neural networks for process control and optimization: two industrial applications.
abstract: The two most widely used neural models, multilayer perceptron (MLP) and radial basis function network (RBFN), are presented in the framework of system identification and control. The main steps for building such nonlinear black box models are regressor choice, selection of internal architecture, and parameter estimation. The advantages of neural network models are summarized: universal approximation capabilities, flexibility, and parsimony. Two applications are described in steel industry and water treatment, respectively, the control of alloying process in a hot dipped galvanizing line and the control of a coagulation process in a drinking water treatment plant. These examples highlight the interest of neural techniques, when complex nonlinear phenomena are involved, but the empirical knowledge of control operators can be learned.
authors.affiliation: Centre de Recherche en Automatique de Nancy (CRAN), UMR CNRS 7039, France. gerard.bloch@esstin.uhp-nancy.fr
authors.forename: Gérard
authors.initials: G
authors.lastname: Bloch
authors.forename: Thierry
authors.initials: T
authors.lastname: Denoeux
mesh_terms: D000465:Algorithms; D000497:Alloys / Q000138:chemical synthesis*; D003198:Computer Simulation; D005246:Feedback; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011786:Quality Control; D012680:Sensitivity and Specificity; D013232:Steel / Q000737:chemistry*; D018508:Water Purification / Q000379:methods*; D015032:Zinc / Q000737:chemistry
chemical_list: D000497:Alloys; D013232:Steel; D015032:Zinc