Neural management for heat and power cogeneration plants

This paper deals with the problem of finding the optimum load allocation on machines and apparatuses in complex Cogeneration Heat and Power (CHP) plants. A methodology based on Neural Networks (NN) has been developed. A database has been populated by using a real plant simulator. Two kinds of plant neural models have been trained, the first consists in an Identification Neural Model (INM) that provides a ‘‘picture’’ of the actual plant status by using monitoring data as input; the second consists in an Optimum Load Allocation Neural Model (OLANM) whose inputs are boundary conditions and outputs the Degrees of Freedom corresponding to the optimum operation set points. To reduce the relevant computational effort required to populate the training databases a sequential chain of neural models has been arranged. The method has been applied to a real cogeneration plant. The developed Plant Optimization Neural Tool (PONT) has shown a good capability to modify load allocation when the status of components and boundary conditions vary. The computational time required is really small (some 500 ms). The accuracy in achieving the solution is comparable with that of traditional physical-empirical plant simulators. These achievements show the potentialities of the neural approach for real time or quasi-real time applications to support plant management decisions.