Using a neural network for estimating plant gradients in real-time optimization with modifier adaptation

Abstract

In the presence of structural plant-model mismatch, standard real-time optimization (RTO) schemes are prone to compute an operation point that does not coincide with the plant optimum. Modifier Adaptation (MA) methods are RTO variants that have the ability to reach plant optimality even in the case of structural plant-model mismatch. However, MA implementations require plant gradient information, which is challenging to obtain. This work proposes a method for estimating plant gradients based on neural networks (radial basis function network - RBFN). Our method is applied for obtaining the gradients of a gas lifted oil well network, which is then optimized using MA. The results show that, even with measurement noise, the gradients are estimated within an adequate precision and the MA method is able to increase production of the well network, reaching the plant optimum without any constraint violations despite the presence of plant-model mismatch.