Ramprasad Yelchuru

MIQP formulation for Controlled Variable Selection in Self Optimizing Control

Abstract In order to facilitate the optimal operation in the presence of process disturbances, the optimal selection of controlled variables plays a vital role. In this paper, we present a Mixed Integer Quadratic Programming methodology to select controlled variables c=Hy as the optimal combinations of fewer/all measurements of the process. The proposed method is evaluated on a toy test problem and on a binary distillation column case study with 41 trays.

Optimal controlled variable selection for individual process units in self optimizing control with MIQP formulations

In order to facilitate optimal operation of process plants in the presence of disturbances, optimal control structure selection is important. In this paper we review the controlled variable selection, c = Hy, where y includes all the measurements. The objective is to find the matrix H such that steady-state operation is optimized when there are disturbances and inputs are adjusted to keep c constant. Several cases are studied such as optimal individual measurements, optimal combinations of fewer/all measurements and combinations of disjoint measurement subsets of fewer/all measurements. The proposed methods are evaluated on a distillation column case study with 41 trays.

Discriminatory learning based performance monitoring of batch processes

This paper proposes a novel approach towards performance monitoring of batch processes that is oriented towards the requirements of real time assessment of batch health and online batch qualification. The proposed approach is based on the use of discriminant analysis and exploits class information that is generally known (but ignored) from the archive of historical batches. Wavelet approximations are shown to provide for a parsimonious representation of the batch profiles. A framework for batch classification that is based on the above discriminatory learning is proposed to facilitate the task of performance monitoring. The developed methods are evaluated on a Penicillin fermentation process for their ability to monitor and to detect the faults both for real time batch qualification as well as for batch release procedures.

Steady State Simulation for Optimal Design and Operation of a GTL Process

In this thesis, the systematic plantwide procedure of Skogestad (2004) is applied to two processes; 1- Post-combustion CO2 capturing processes, 2- Natural gas to liquid hydrocarbons (GTL) plants, in order to design economically efficient control structures, which keep the processes nearoptimum when disturbances occur. Because of the large magnitude of energy consumption in both these processes, optimal operation is of great importance. The self-optimizing concept, which is the heart of the plantwide procedure is used to select the right controlled variables in different operational regions, which when they are kept constant, indirectly give the operation close to optimum. The optimal is to reconfigure the self-optimizing control loops when the process is entered into a new active constraint region, but we try to arrive at a simple/single control structure, which does not need switching, where a reasonable loss in operating economic objective function is accepted. The CO2 capturing process studied here is an amine absorption/stripping system. The chosen objective function for this process is first to minimize the energy requirement while fixed CO2 recovery of 90% is met. This leads to one unconstrained degree of freedom. Maximum gain rule is applied and a temperature close to the top of the stripper is found as the best controlled variable. Further, we introduce penalty on CO2 amount released to the atmosphere, and this results in two unconstrained degrees of freedom. CO2 recovery and a temperature close to the top of the stripper are found as the best individual controlled variables in low feedrate. In higher flue gas flowrates, stripper heat input saturates and the self-optimizing method is repeated to select the right controlled variable for the remaining degree of freedom. We validate the propose control structures using dynamic simulations, where 5 different alternatives including decentralized control loops and multivariable controller are studied. We finally achieve a simple control structure, which handles a wide range of change in throughput and keeps the process close to optimum without the need for switching the control loops or updating the controlled variables setpoints by a costly real time optimizer. The GTL process modeled in this thesis includes an auto-thermal reformer (ATR) for synthesis gas production and a slurry bubble column reactor (SBCR) for the Fischer-Tropsch (FT) reactions. The FT products distribution is determined using a well-known Anderson- Schultz- Flory (ASF) model, where carbon component in CO (consumption rate is found based on the proposed rate by Iglesia et al.) is distributed to a range of hydrocarbons. ASF is a function of chain growth probability and the chain growth is a function of H2/CO ratio. We study different scenarios for chain growth and we arrive at a suitable model for optimal operation studies. The optimal operation is considered in two modes of operation. In mode I, natural gas feedrate is assumed given and in mode II, natural gas feedrate is also a degree of freedom. After optimization, in both modes, there are three unconstrained degrees of freedom. The best individual self-optimizing controlled variables are found and since the worst-case loss value is rather notable, combination of measurements is done, which reduces the loss significantly. Mode II happens when oxygen flowrate capacity reaches the maximum and we show that operation in mode II in this case is in snowballing region where operation should be avoided. Operation at maximum oxygen flowrate capacity is where maximum practical profit can be achieved.

Quantitative methods for optimal regulatory layer selection

Controlled variables selection based on economic objectives using self optimizing concepts are developed. In this paper, we extend the self optimizing control ideas to find optimal controlled variables in the regulatory layer. The regulatory layer is designed to facilitate stable operation, to regulate and to keep the operation in the linear operating range and its performance is here quantified using the state drift criterion. Quantitative methods are proposed and evaluated on a distillation column case study with 41 stages that minimize state drift in composition states to obtain optimal regulatory layer with 1,2 and more closed loops.