Abstract
This paper is concerned with the real-time optimization (RTO) of chemical plants, i.e., the optimization of the steady-state operating points during operation, based on inaccurate models. Specifically, modifier adaptation is employed to cope with the plant-model mismatch, which corrects the plant model and the constraint functions by bias and gradient correction terms that are computed from measured variables at the steady-states of the plant. This implies that the sampling time of the iterative RTO scheme is lower-bounded by the time to reach a new steady-state after the previously computed inputs were applied. If analytical process measurements (PAT technology) are used to obtain the steady-state responses, time delays occur due to the measurement delay of the PAT device and due to the transportation delay if the samples are transported to the instrument via pipes. This situation is quite common because the PAT devices can often only be installed at a certain distance from the measurement location. The presence of these time delays slows down the iterative real-time optimization, as the time from the application of a new set of inputs to receiving the steady-state information increases further. In this paper, a proactive perturbation scheme is proposed to efficiently utilize the idle time by intelligently scheduling the process inputs taking into account the time delays to obtain the steady-state process measurements. The performance of the proposed proactive perturbation scheme is demonstrated for two examples, the Williams–Otto reactor benchmark and a lithiation process. The simulation results show that the proposed proactive perturbation scheme can speed up the convergence to the true plant optimum significantly.
Highlights
There is a strong interest in the process industries to identify the best steady-state operating conditions of their processes such that they are operated at their economic optimum while meeting constraints on product qualities, emissions, and equipment capabilities
In modifier adaptation with quadratic approximation (MAWQA) with guaranteed model adequacy (GMA), the plant gradients are estimated from past inputs and steady-state measurements using quadratic approximation (QA)
We address iterative real-time optimization (RTO) with plant-model mismatch in the presence of a time delay caused due to the positioning of an analytic measurement device at some distance from the place where the process stream is sampled
Summary
There is a strong interest in the process industries to identify the best steady-state operating conditions of their processes such that they are operated at their economic optimum while meeting constraints on product qualities, emissions, and equipment capabilities. In ISOPE scheme, in addition to updating the model parameters in each iteration, the objective function of the model based optimization problem is modified iteratively by adding bias and gradient correction terms so that the solution converges iteratively to the optimum of the real plant. These correction terms ( called modifiers) are estimated from the available measurements. Modifier adaptation-based iterative RTO methods require steady-state process measurements to compute a new input that provides a lower limit to the time between iterates. The performance of the proposed scheme is analyzed using the Williams–Otto reactor benchmark and a lithiation reaction case study
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 call
