David Zumoffen

Plant-wide control strategy applied to the Tennessee Eastman process at two operating points

Abstract This work presents a new plant-wide control strategy able to be applied on large scale chemical plants. It is based on an extension of the non square relative gain array (NRG) theoretical concepts, introduced by Chang and Yu (1990), and the generalized relative disturbance gain (GRDG) presented in Chang and Yu (1992). The extension of the NRG is useful for searching the best group of controlled variables (CVs) independently of the problem dimensionality. Meanwhile, the extension of the GRDG allows configure the loops pairing by considering the trade-off between servo and regulator behavior. It can be done thanks to define a proper function, named net load effect, accounting both set point and disturbances effects. Even though these concepts are not new, the main contribution of this paper is the selection of the adequate objective function. It is mathematically expressed in a new way, in terms of Frobenius norm of specific matrices related with the models of the plant and very useful for evaluating the process interaction. Then, it drives the search supported by genetic algorithms (GA), which evaluates all the possible combinations of input–output variables. It allows to solve successfully and with less computational effort the combinatorial optimization problem, even though the high dimension usually involved in large scale chemical plants. The use of the relative gain array (RGA) can also be considered for pairing purpose, but in some cases it could drive to a less effective structure. The use of relative normalized gain array (RNGA) for pairing the selected CVs with the most suitable MVs is able to lead to best control structures only if a dynamic model of the plant is available. Therefore, it must be emphasized that this approach is developed for working in cases where only steady-state plant information is available. However, if a dynamic model is disposable too the algorithm is extended to use it. In addition, a mathematical demonstration is presented so as to understand why is possible to find a well conditioned control structure. The methodology is tested in the Tennessee Eastman (TE) process at the base case proposed by Downs and Vogel (1992), and at an optimized working point presented by Ricker (1995). Both working points show two quite different scenarios. Thus, a set of dynamic simulations for both cases and the hardware requirements compared to the previous suggested are given to proof the capacity of this approach.

Oversizing Analysis in Plant-wide Control Design for Industrial Processes

Abstract In this work, an alternative plant-wide control design approach based on oversizing analysis is presented. The overall strategy can be divided in two main sequential tasks: 1 – defining the optimal decentralized control structure, and 2 – setting the controller interaction degree and its implementation. Both problems represent combinatorial optimizations based on multi-objective functional costs and were solved efficiently by genetic algorithms. The first task defines the optimal selection of controlled and manipulated variables simultaneously, the input–output pairing, and the overall controller dimension in a sum of square deviations context. The second task analyzes the potential improvements by defining the controller interaction degree via the net load evaluation approach. In addition, some insights are given about the feasibility (implementation load) of these control structures for a decentralized or centralized framework. The well-known Tennessee Eastman (TE) process is selected here for sake of comparison with other multivariable control designs.

Improvements on multiloop control design via net load evaluation

Abstract The plant-wide control problem is a very important topic in process control. A particular control structure design will define (restrict) the future operability degree for the plant under study. Classical control policies (decentralized or full) are not always the best solution. In this context a systematic and generalized strategy to solve the multivariable plant-wide control problem is proposed here. The methodology called minimum square deviation (MSD) considers several points such as the optimal controlled variables (CVs) selection based on the sum of square deviation (SSD) and controller structure design supported by net load evaluation (NLE) analysis. The overall problem is combinatorial and is solved by accounting several steady-state tools and new indexes minimizing the heuristic load. Four well-known case studies are presented and other approaches taken from the literature are accounted for the sake of comparison. A robust stability test, μ -tools, is also performed for concluding about the control policies.

Advanced control for fuel cells connected to a DC/DC converter and an electric motor

The transient behavior of a polymer electrolyte membrane fuel cells (PEMFC) system under an improved adaptive predictive control with robust filter (APCWRF) is analyzed using a nonlinear dynamic, control-oriented model. Sudden changes in the stack current are associated with the abrupt changes in the power demanded by the electric motor of a vehicle, powered by the PEMFC. The APCWRF is designed for controlling the compressor motor voltage. Because of the wide working range the control algorithm is improved accounting three different zones supported by three nominal models. It is specially thought to achieve a better efficiency and to maintain the necessary level of the oxygen in the cathode to prevent short circuit and membrane damage. A DC/DC converter is connected to the electric motor. It is used as an actuator in a cascade control loop to regulate the torque output of a DC electric motor with a PI controller in the external loop. Several results are presented considering the PEMFC with the APCWRF showing its potentiality for a wide working range imposed by two types of DC motors.

Decentralized plantwide control strategy for large-scale processes. Case study: Pulp mill benchmark problem

Abstract The plantwide control (PWC) complexity increases for highly-integrated and large-scale chemical processes. This work presents a novel framework for decentralized PWC which includes: (i) the selection of the controlled variables (CVs), (ii) the pairing between the manipulated variables (MVs) and the CVs, and (iii) the determination of the controller algorithms as well as their tuning parameters for closed-loop operation. The proposal is to solve the steps (i) and (ii) simultaneously, driving the selection of the most effective PWC structure from a Pareto optimal set. Here, algorithms based only on steady-state information are considered to give a systematic procedure which tries to minimize the use of heuristic considerations. Genetic algorithms (GA) and the Hungarian algorithm (HA) are used here because they provide a good trade-off between computational effort and acceptable results. The proposed methodology is completely tested in a pulp mill benchmark and compared with a previous one.

Improvements on Noninvasive Blood Glucose Biosensors Using Wavelets for Quick Fault Detection

Noninvasive blood glucose sensors are still under development stage considering that they are far from being suitable for use in anartificial pancreas. The latter has three main parts: the blood glucose sensor, the insulin pump and the controller. However, for the biosensor analyzed here, some common failures such as signal shifts and unreal picks were found. They must be taken into account, for computing the correct insulin dosage for diabetic persons. Hence, a fault detection system based on discrete wavelets transform (DWT) is applied here. The main idea is, when the fault occurs, to do a proper measurement compensation for sending the corrected value to the predictive functional controller (PFC) algorithm. The study is done by reproducing the fault on the blood glucose measurements. They are obtained from a mathematical model of the endocrine system of an adult diabetic patient. This model was approved by the FDA in 2008. Then, the simulation environment includes faulty blood glucose measurements and a fault diagnosis and identification (FDI) system based on DWT. The FDI system gives to the PFC algorithm the correct information to turn it into a fault-tolerant controller (FTC). The main goal is to deliver the correct insulin dosage to the patient.

Optimal Sensor Location for Chemical Process Accounting the Best Control Configuration

Abstract In this work a new methodology for solving simultaneously the problems of optimal sensor location and control structure selection for large scale chemical processes is presented. Here, it is considered the need of guaranteing the best plant-wide control structure before answering about which is the best sensor net able for achieving that objective. In this work it is demonstrated the importance of answer both questions as an integrated problem because of the strong impact in the initial investment and the future controlled process performance. Most of the previous works in this area analyze these problems as separated subjects. Here, genetic algorithms (GA) are used because they represent a valuable tool for support the decisions about the sensor placement, possible pairing of input output variables among a great number of combinations, since the interaction effect point of view. It allows to avoid the expert knowledge as decision criteria for pairing selection. The preliminary study is done on a simplified plant model obtained by subspace identification techniques (4sid). The final testing is performed on the rigorous dynamic model with the obtained plant-wide structure where the controllers tuning is performed through the internal model control (IMC) theory. The well-known case of the Tennessee Eastman (TE) benchmark is adopted for testing the methodology described here and compared with other strategies.

A New Systematic Approach to Fi nd Plantwide Control Structures

Abstract A new systematic approach for addressing the problem of the plant-wide control structure, supported by steady-state information is presented. It is tested in a reactor/separator with recycle plant. Several authors have presented different control alternatives for this kind of plant. However, most of them mainly focused on the optimum energy consumption or in the regulator problem only. In both cases the decision of the final control structure was adopted based on several heuristic concepts. In this work both objectives are considered avoiding any heuristic concepts. The approach consists meanly on three steps: optimization, stabilization, and final pairing between manipulated and controlled variables focused on rejecting the most critical disturbances. In addition, the reactor design is subject to ensure quality product specification, cost investment together with the overall plant controllability. Finally, the dynamic model of the plant is used for testing the potentiality of the proposed control structure. Hence, a multivariable tuning procedure, based on internal model control (IMC), is performed for evaluating the dynamic closed loop responses under the worst disturbances scenarios.

Plant-Wide Control Based on Minimum Square Deviation

Abstract In this work a new systematic and generalized strategy to solve the MIMO plant-wide control problem is proposed. The methodology called Minimum Square Deviation (MSD) considers several points such as the optimal sensor location (OSL) based on the sum of square deviation (SSD) and the control structure selection (CSS) based on net load evaluation (NLE) problems simultaneously. Particularly, this work focuses on selecting the best MIMO control structure by using a new steady-state index called NLE. Thus, alternative control structures can be obtained through different interaction levels and defining a corresponding performance improvement. Two well-known chemical process are proposed here for testing this methodology. In addition, a robust stability analysis applying the classical μ-tool is performed by considering both parametric and unmodeled dynamic uncertainties.

Testing PFC Controller On A Well Validated In Silico Model of a Type I Diabetic Patient

Abstract Diabetes technology has been focused since three decades ago on developing the artificial pancreas through several closed-loop control algorithms linking glucose measurements and insulin delivery. This work is focused on rigorously analyzing the Predictive Functional Control (PFC) algorithm capabilities for deciding about the correct insulin dosage under everyday circumstances. The study is done by applying the PFC in a recently developed model of the endocrine system, approved by the FDA in 2008, as a substitute to animal trial. The platform used here consists only of a limited number of patients: 10 children, 10 adolescents, and 10 adults. To realistically represent the full closed loop system, a model of a subcutaneous glucose sensor was added and the constraints related to the insulin pump was taken into account by the predictive controller. The performance of the controller, with and without the sensor model, was evaluated by means of the Control Variability Grid Analysis (CVGA) technique and the results were satisfactory in all patients.