Data-based fault-tolerant model predictive controller an application to a complex dearomatization process

The tightening global competition during the last few decades has been the driving force for the optimisation of industrial plant operations through the use of advanced control methods, such as model predictive control (MPC). As the occurrence of faults in the process measurements and actuators has become more common due to the increase in the complexity of the control systems, the need for fault-tolerant control (FTC) to prevent the degradation of the controller performance, and therefore the better optimisation of the plant operations, has increased. Traditionally, the most actively studied fault detection and diagnosis (FDD) components of the FTC strategies have been based on model-based approaches. In the modern process industries, however, there is a need for the data-based FDD components due to the complexity and limited availability of mechanistic models. Recently, active FTC strategies using fault accommodation and controller reconfiguration have become popular due to the increased computation capacity, easier adaptability and lower overall implementation costs of the active FTC strategies. The main focus of this thesis is on the development of an active data-based fault-tolerant MPC (FTMPC) for an industrial dearomatization process. Three different parallel-running FTC strategies are developed that utilise the data-based FDD methods and the fault accommodation- and controller reconfiguration-based FTC methods. The performances of three data-based FDD methods are first compared within an acknowledged testing environment. Based on the preliminary performance testing, the best FDD method is selected for the final FTMPC. Next, the performance of the FTMPC is validated with the simulation model of the industrial dearomatization process and finally, the profitability of the FTMPC is evaluated based on the results of the evaluation. According to the testing, the FTMPC performs efficiently and detects and prevents the effects of the most common faults in the analyser, flow and temperature measurements, and the controller actuators. The reliability of the MPC is increased and the profitability of the dearomatization process is enhanced due to the lower off-spec production

Statistical process monitoring of a multiphase flow facility

Industrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms

Malliprediktiivinen säädin Tennessee Eastman prosessille

This thesis aims to design a multivariable Model Predictive Control (MPC) scheme for a complex industrial process. The focus of the thesis is on the implementation and testing of a linear MPC control strategy combined with fault detection and diagnosis methods. The studied control methodology is based on a linear time invariant state-space model and the quadratic programming optimization procedure. The control scheme is realized as a supervisory one, where the MPC is used to calculate the optimal set point trajectories for the lower level PI controllers, thus aiming to decrease the fluctuations in the end product flows. The Tennessee Eastman (TE) process is used as the testing environment. The TE process is a benchmark based on a real process modified for testing. It has five units, four reactants, an inert, two products and a byproduct. The control objective is to maintain the production rate and the product quality at the desired level. To achieve this, the MPC implemented in this thesis gives setpoints to three stabilizing PI control loops around the reactor and the product stripper. The performance of the designed control systems is evaluated by inducing process disturbances, setpoint changes, and faults for two operational regimes. The obtained results show the efficiency of the adopted approach in handling disturbances and flexibility in control of different operational regimes without the need of retuning. To suppress the effects caused by faults, an additional level that provides fault detection and controller reconfiguration should be developed as further research.Tämän diplomityön tavoite on suunnitella monimuuttujainen-malliprediktiivinen säädin (MPC) teolliselle prosessille. Diplomityö keskittyy toteuttamaan ja testaamaan lineaarisen MPC strategian, joka yhdistettynä vikojen havainnointiin ja tunnistukseen sekä uudelleen konfigurointiin voidaan laajentaa vikasietoiseksi. Tutkittu säätöstrategia perustuu lineaariseen ajan suhteen muuttumattomaan tilataso-malliin ja neliöllisen ohjelmoinnin optimointimenetelmään. Säätö on toteutettu nk. ylemmän tason järjestelmänä, eli MPC:tä käytetään laskemaan optimaaliset asetusarvot alemman säätötason PI säätimille, tavoitteena vähentää vaihtelua lopputuotteen virroissa. Tennessee Eastman (TE) prosessia käytetään testiympäristönä. TE on testiprosessi, joka perustuu todelliseen teollisuuden prosessiin ja jota on muokattu testauskäyttöön sopivaksi. Prosessissa on viisi yksikköä, neljä lähtöainetta, inertti, kaksi tuotetta ja yksi sivutuote. Säätötavoite on ylläpitää haluttu taso tuotannon määrässä ja laadussa. Tämän saavuttamiseksi tässä diplomityössä toteutettu MPC antaa asetusarvoja kolmelle stabiloivalle PI-säätimelle reaktorin ja stripperin hallinnassa. Säätösysteemin suorituskykyä arvioitiin aiheuttamalla prosessiin häiriöitä, asetusarvon muutoksia ja vikoja eri operatiivisissa olosuhteissa. Saavutetut tulokset osoittavat valitun menetelmän tehokkuuden häiriöiden käsittelyyn ja joustavaan säätöön eri olosuhteissa. Tutkimuksen jatkokehityksenä vikojen vaikutuksen vaimentamiseksi säätöön tulisi lisätä taso, joka havaitsee viat ja uudelleen konfiguroi säätimen sen mukaisesti

Outline of a fault diagnosis system for a large-scale board machine

Global competition forces process industries to continuously optimize plant operation. One of the latest trends for efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition, and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed.Peer reviewe

Nonlinear dynamic process monitoring using kernel methods

The application of kernel methods in process monitoring is well established. How- ever, there is need to extend existing techniques using novel implementation strate- gies in order to improve process monitoring performance. For example, process monitoring using kernel principal component analysis (KPCA) have been reported. Nevertheless, the e ect of combining kernel density estimation (KDE)-based control limits with KPCA for nonlinear process monitoring has not been adequately investi- gated and documented. Therefore, process monitoring using KPCA and KDE-based control limits is carried out in this work. A new KPCA-KDE fault identi cation technique is also proposed. Furthermore, most process systems are complex and data collected from them have more than one characteristic. Therefore, three techniques are developed in this work to capture more than one process behaviour. These include the linear latent variable-CVA (LLV-CVA), kernel CVA using QR decomposition (KCVA-QRD) and kernel latent variable-CVA (KLV-CVA). LLV-CVA captures both linear and dynamic relations in the process variables. On the other hand, KCVA-QRD and KLV-CVA account for both nonlinearity and pro- cess dynamics. The CVA with kernel density estimation (CVA-KDE) technique reported does not address the nonlinear problem directly while the regular kernel CVA approach require regularisation of the constructed kernel data to avoid com- putational instability. However, this compromises process monitoring performance. The results of the work showed that KPCA-KDE is more robust and detected faults higher and earlier than the KPCA technique based on Gaussian assumption of pro- cess data. The nonlinear dynamic methods proposed also performed better than the afore-mentioned existing techniques without employing the ridge-type regulari- sation

Developing an online predictor to predict product sulfur concentration for HDS unit

Hydrodesulfurization (HDS) is an important process in refining industries. Advanced control system (e.g. model predictive controller) requires on-line measurement of the product sulfur at the reactor outlet. However, most HDS processes do not have a sulfur analyzer at the reactor outlet. In order to predict product sulfur concentration usually a data based sulfur predictor is developed. Performance of data based predictor is usually poor since some of the input parameters (e.g. feed sulfur concentration) are unknown. The objective of this thesis is to overcome these limitations of data based predictors and develop an online product sulfur predictor for HDS unit. In this thesis, a hybrid model is proposed, developed and validated (using industrial data), which could predict product sulfur concentration for online HDS system. The proposed hybrid structure is a combination of a reaction kinetics based HDS reactor model and an empirical model based on support vector regression (SVR). The mechanistic model runs in off-line mode to estimate the feed sulfur concentration while the data based model uses the estimated feed sulfur concentration and other process variables to predict the product sulfur concentration. The predicted sulfur concentration can be compared with the lab measurements or sulfur analyzer located further downstream of the process at the tankage. In case there is a large discrepancy, the predictor goes to a calibration mode and uses the mechanistic model to re-estimate the feed sulfur concentration. The detailed logic for the online prediction is also developed. Finally a Matlab based Graphical User Interface (GUI) has been developed for the hybrid sulfur predictor for easy implementation to any HDS process