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Abstract

A computer neural network process measurement and control system and method uses real-time output data from a neural network to replace a sensor or laboratory input to a controller. The neural network can use readily available, inexpensive and reliable measurements from sensors as inputs, and produce predicted values of product properties as output data for input to the controller. The system and method overcome process deadtime, measurement deadtime, infrequent measurements, and measurement variability in laboratory data, thus providing improved control. An historical database can be used to provide a history of sensor and laboratory measurements to the neural network. The neural network can detect the appearance of new laboratory measurements in the history and automatically initiate retraining, on-line and in real-time. The system and method can use either a regulatory controller or a supervisory control architecture. A modular software implementation simplifies the building of multiple neural networks, and also optionally provides other control functions, such as supervisory controllers, expert systems, and statistical data filtering, thus allowing powerful extensions of the system and method. Template specification for the neural network, and data specification using data pointers allow the system and method to be more easily implemented.

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First Claim