PLIO: a generic tool for real-time operational predictive optimal control of water networks

This paper presents a generic tool, named PLIO, that allows to implement the real-time operational control of water networks. Control strategies are generated using predictive optimal control techniques. This tool allows the flow management in a large water supply and distribution system including reservoirs, open-flow channels for water transport, water treatment plants, pressurized water pipe networks, tanks, flow/pressure control elements and a telemetry/telecontrol system. Predictive optimal control is used to generate flow control strategies from the sources to the consumer areas to meet future demands with appropriate pressure levels, optimizing operational goals such as network safety volumes and flow control stability. PLIO allows to build the network model graphically and then to automatically generate the model equations used by the predictive optimal controller. Additionally, PLIO can work off-line (in simulation) and on-line (in real-time mode). The case study of Santiago-Chile is presented to exemplify the control results obtained using PLIO off-line (in simulation). © IWA Publishing 2011.Research in this group is partially supported by by the Generalitat de Catalunya Research Committee, under grant ref. 2009/SGR/1491, by the Spanish Ministry of Science and Technology under grant WATMAN (CICYT DPI2009-13744) and the EU project WIDE (FP7-IST-224168).Peer Reviewe

Controle de nível de tanques interativos baseados em técnicas de redes neurais artificiais

Orientador: Ana Maria Frattini FiletiDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: O controle de nível de tanques interativos a partir da vazão é um sistema MIMO (multiple input multiple output), que envolve uma série de desafios como não linearidades acentuadas, interação entre as variáveis do processo e tempos mortos e, por isso, nem sempre pode ser controlado por técnicas de controle convencionais como o PID. Rede neurais artificiais (RNA) são uma técnica de processamento paralelo capaz de capturar relações bastante não lineares entre várias variáveis de entradas e várias variáveis de saídas. Dessa forma, diversas técnicas de controle utilizando RNA tem sido propostas para processos em que o controle feedback tradicional possa não funcionar satisfatoriamente. O presente trabalho visava testar a viabilidade experimental de duas técnicas de controle baseadas em redes neurais aplicadas no controle de nível em tanques interativos: o controle preditivo baseado em redes neurais (MPC-RNA), que consiste em utilizar um modelo neural do processo e um algoritmo de otimização para obter uma performance satisfatória; e o controle neural inverso, que é uma técnica de controle baseada na predição da variável manipulada diretamente das variáveis controladas. Além disso, o trabalho também visava comparar a performance das duas técnicas mencionadas com a performance do controlador PID convencional. Os experimentos foram realizados no sistema de tanques interativos do Laboratório de controle e automação (LCAP) na Unicamp. Ambos os níveis dos tanques acoplados eram controlados a partir da manipulação das potências das duas bombas que regulavam as vazões. Uma válvula intermediária manual conectava os tanques e gerava não linearidades, bem como interação entre os níveis, o que dificultava o controle PID. A aplicação experimental das três técnicas mencionadas foi feita por meio de um programa desenvolvido em MATLAB® e um CLP foi utilizado para fazer a aquisição dos dados da planta. Uma comparação entre as duas técnicas de controle baseadas em redes neurais mostrou que o controle neural inverso não foi capaz de seguir o setpoint satisfatoriamente, já que a técnica deixou um offset. Enquanto isso, a técnica MPC-RNA foi capaz de seguir o setpoint mais rapidamente e com menores overshoots do que o PID. A performance melhor do MPC-RNA em relação ao PID pode ser atribuída a capacidade do algoritmo de controle preditivo de minimizar os desvios entre a saída desejada e predita, e a habilidade das redes neurais artificiais de lidar com não linearidades e interação entre variáveis manipuladas e controladas. Além disso, o controlador MPC-RNA acopla a estratégia feedback e feedforward, dessa forma, compensando desvios entre o valor real e o valor predito a partir do modelo distúrbioAbstract: The level control of interactive tanks adjusting flow rates is a multiple input multiple output (MIMO) system that poses many challenges in the control problem, such as nonlinearities, interactions between manipulated and process variables and dead times. Therefore, conventional techniques such as the Proportional Integral Derivative (PID) controller might not work properly in this process. Artificial neural network (ANN) is a parallel processing technique that can capture highly nonlinear relationships among input and output variables. Hence, some control techniques that use ANN have been proposed for processes in which traditional feedback techniques may not work properly. This work aimed to test the experimental feasibility of two control techniques based on artificial neural networks applied to level control in coupled tanks: the model predictive control based on neural modeling (MPC-ANN) and an inverse neural network control. In the first strategy, an artificial neural network model of the process and an optimization algorithm are used to derive a satisfactory error performance. The second one is a control technique based on predicting the manipulated variables straight from the measurements of the process variables. Moreover, this work aimed to compare the performance of the two techniques mentioned with the conventional PID. The experiments were carried out using interactive tanks set up in of the Laboratory of Control and Automation at the University of Campinas (UNICAMP). Both levels of coupled tanks were to be controlled by manipulating the power of the two pumps that regulates output flow rates. An intermediate manual valve connected the tanks, generating nonlinearities and interaction between the levels, which made the success of PID control more difficult. The experimental application of the three mentioned techniques was performed with algorithm developed in MATLAB® and using a PLC to acquire the plant data. The comparison between the two-control neural network control techniques showed that the inverse neural control was not capable to track the set-point satisfactorily since it left an offset while the MPC-ANN was capable to track the set-point faster than the PID and it left smaller overshoots than the PID. The MPC-ANN performed better than the PID due to the capacity of model predictive control algorithm to minimize the deviations between the desired and predicted outputs, and the ability of artificial neural networks to deal with nonlinearities and interactions between manipulated and controlled variables. Besides, MPC-ANN couples feedback and feedforward strategy so it compensates model plant mismatches with the disturbance modelMestradoEngenharia QuímicaMestre em Engenharia Química1776016CAPE

PLIO: a generic tool for real-time operational predictive optimal control of water networks

This paper presents a generic tool, named PLIO, that allows to implement the real-time operational control of water networks. Control strategies are generated using predictive optimal control techniques. This tool allows the flow management in a large water supply and distribution system including reservoirs, open-flow channels for water transport, water treatment plants, pressurized water pipe networks, tanks, flow/pressure control elements and a telemetry/telecontrol system. Predictive optimal control is used to generate flow control strategies from the sources to the consumer areas to meet future demands with appropriate pressure levels, optimizing operational goals such as network safety volumes and flow control stability. PLIO allows to build the network model graphically and then to automatically generate the model equations used by the predictive optimal controller. Additionally, PLIO can work off-line (in simulation) and on-line (in real-time mode). The case study of Santiago-Chile is presented to exemplify the control results obtained using PLIO off-line (in simulation)Peer ReviewedPostprint (author’s final draft

Modelling and decentralized model predictive control of drinking water networks: the Barcelona case study

In this report, MPC strategies have been designed and tested for the global centralized and decentralized control of drinking water networks. Test have been performed in order to highlight the advantages of having a partition of a complex network in several subsystems. Despite the possible suboptimal solution of the optimization problems from the global point of view, the clear gain related to the computation times and loads has been demonstrated by means of the simulations and test developed here. The high correlation between system elements, i.e., the strong coupling of the network, makes impossible to have independent subsystems to be controlled by using a set of decoupled MPC controllers. Moreover, the necessity of a hierarchy scheme is discussed and interesting results are obtained from the mixture of techniques giving rise to a control law sharing decentralized and hierarchical features.Preprin

Identification and energy optimization of supercritical carbon dioxide batch extraction

Abstract. The emergence of green chemistry, aiming to increase ecological and energy efficiency of processes, has gained supercritical fluid extraction increasing amounts of prominence. Traditional extraction methods utilize hazardous chemicals, have low extractive yield in relation to energy consumption, and produce large amounts of organic waste. Supercritical fluid extraction offers improvements to these challenges in the form of reduced processing energy inputs and an alternative solvent approach. Carbon dioxide is the most commonly employed solvent in supercritical fluid extraction due to the many advantages it brings over other solvents including price, smaller environmental and health risks, and simple separation. The research on data-driven system identification and advanced process control of supercritical extraction has been very scarce. According to past research, the control of supercritical is mostly carried out using basic, non-model-based control schemes. Challenges such as coupling between control loops and nonlinearities of fluid and process dynamics create major challenges for the basic control schemes. With advanced control methods, it could be possible to address these challenges better. Model-based control schemes, in theory, pose many advantages and benefits over basic control, such as improved production economics, optimized product quality and yields, and further possibilities in model-driven research and development. The goal of this thesis was to improve control performance and optimize energy consumption a pilot-scale batch supercritical carbon dioxide extraction process by utilizing model predictive control strategies. The modeling of the unit processes of the target batch extraction was based on measurement data gathered by experimental design and careful examination of the system. The models were utilized in a simulator developed in this study. The arrangement of the implemented experimental design (central composite design, CCD) allowed the exploitation of linear regression analysis; the results of which indicated the existence of possible nonlinearities between steady-state electricity consumption and the operative variables of the process. Model predictive control schemes were developed in a simulator environment for carbon dioxide pressure control, carbon dioxide volumetric flow control, extractor temperature control and separator temperature control. The developed control schemes showed major improvements in control performance of the simulated unit processes, resulting in significant decreases in total electricity and heating water consumptions (up to 25% and 21% respectively). Model predictive control also proved to be quite flexible over the base control system for some processes, providing the possibility of modifying control performance by simple tuning adjustments. The simulated control strategies demonstrate the benefits of model-based control in terms of process energy efficiency and economy. In addition to these results, the identified process and controller models have further potential in future research on control and process developments of supercritical fluid extraction.Ylikriittisen hiilidioksidipanosuuton identifiointi ja energiaoptimointi. Tiivistelmä. Prosessien ekologisuuden ja energiatehokkuuden lisäämiseen tähtäävä vihreä kemia edistää ylikriittisen uuton merkittävyyttä yhä enemmän. Perinteiset erotusmenetelmät käyttävät haitallisia kemikaaleja, niillä on alhainen uuteainesaanto suhteessa energian kulutukseen, ja ne tuottavat suuren määrän orgaanista jätettä. Ylikriittinen uutto tarjoaa parannuksia näihin haasteisiin prosessointienergian kulutuksen vähentymisen ja vaihtoehtoisen liuotinratkaisun muodossa. Hiilidioksidi on yleisimmin käytetty liuotin ylikriittisessä uutossa, koska sillä on monia etuja muihin liuottimiin verrattuna, mukaan lukien hinta, pienemmät ympäristö- ja terveysriskit sekä yksinkertainen erottaminen. Ylikriittiseen uuttoprosessiin liittyvän datapohjaisen identifioinnin ja kehittyneen säädön tutkimus on ollut hyvin vähäistä. Aiempien tutkimusten perusteella ylikriittisen uuton säätö toteutetaan pääasiassa perustason ei-mallipohjaisilla säätörakenteilla. Ohjaussilmukoiden vuorovaikutukset sekä neste- ja prosessidynamiikan epälineaarisuudet luovat suuria haasteita perussäätörakenteille. Kehittyneillä säätömenetelmillä olisi mahdollista käsitellä näitä haasteita paremmin. Mallipohjaiset säätöratkaisut tuovat teoriassa useita etuja ja hyötyjä perussäätöön verrattuna parantuvan tuotantoekonomian, optimoidun tuotelaadun ja -saannon sekä malliperusteisen tutkimuksen ja -kehityksen lisämahdollisuuksien muodossa. Tämän työn tavoitteena oli nostaa pilottikoon ylikriittisen hiilidioksidipanosuuttoprosessin säädön suorituskykyä ja optimoida energiankulutusta hyödyntämällä mallipredikriivisiä säätöstrategioita. Tutkimuksen kohteena olleen panosuuton yksikköprosessien mallinnus perustui koesuunnittelulla kerättyyn mittausaineistoon ja järjestelmän huolelliseen tarkkailuun. Malleja hyödynnettiin työssä kehitetyssä prosessisimulaattorissa. Toteutettu koessunnitelma (central composite design, CCD) mahdollisti lineaarisen regressioanalyysin hyödyntämisen, jonka tulokset osoittivat mahdollisten epälineaarisuuksien olemassaolon prosessin vakaan tilan sähkönkulutuksen ja operatiivisten muuttujien välillä. Malliprediktiiviset säätörakenteet kehitettiin simulaatioympäristössä hiilidioksidin paineen, hiilidioksidin tilavuusvirtauksen, uuttoreaktorin lämpötilan, ja erottajan lämpötilan säädöille. Kehitetyt säätörakenteet toivat suuria säätöparannuksia simuloituihin yksikköprosesseihin, johtaen merkittäviin vähennyksiin käyttösähkön- ja lämmitysveden kulutuksissa (vastaavat vähennykset 25 % ja 21 % saakka). Malliprediktiivinen säätö osoitti myös joustavuutensa perusäätöjärjestelmään verrattuna joissakin prosesseissa, mahdollistaen säätösuorituskyvyn modifioinnin yksinkertaisilla viritysmuutoksilla. Simuloidut säätöstrategiat havainnollistavat mallipohjaisen säädön mahdollisia hyötyjä prosessin energiatehokkuuden ja taloudellisuuden kannalta. Näiden tulosten lisäksi identifioiduilla prosessi- ja säädinmalleilla on lisäpotentiaalia tulevaisuuden ylikriittisen uuton säädön tutkimuksissa ja prosessikehityksissä

A survey of recent advances in fractional order control for time delay systems

Several papers reviewing fractional order calculus in control applications have been published recently. These papers focus on general tuning procedures, especially for the fractional order proportional integral derivative controller. However, not all these tuning procedures are applicable to all kinds of processes, such as the delicate time delay systems. This motivates the need for synthesizing fractional order control applications, problems, and advances completely dedicated to time delay processes. The purpose of this paper is to provide a state of the art that can be easily used as a basis to familiarize oneself with fractional order tuning strategies targeted for time delayed processes. Solely, the most recent advances, dating from the last decade, are included in this review

The design and evaluation of a PLC-based model predictive controller for application in industrial food processes

Model Predictive Control (MPC) is a viable control strategy for industrial processes that display relatively large variations in the process variable, have complex process variable interactions, or display a large amount of process deadtime. The objective of using MPC in manufacturing is to reduce overall process variability, the result being an increase in process accuracy, precision and efficiency. This study focused on the implementation of model predictive control techniques on an industrial sugar cooking process. The goal was to implement a successful MPC solution directly on a programmable logic controller (PLC) rather than on a personal computer (PC). Although there are many commercially available MPC controllers for implementation on a stand-alone PC, to date there are no control packages for realizing model-based control techniques directly on the ubiquitous PLC. This study implemented and evaluated three PC-based, commercial MPC technologies for the sugar cooking process, and a new model state feedback (MSF) MPC implementation directly on Rockwell Automation\u27s Allen-Bradley ControlLogix ® PLC. A standard proportional-integral-derivative (PID) control implementation was used as a baseline for comparing the MPC strategies. There were three main areas on which the overall comparative analysis focused. These comparison areas were the dynamic response of each strategy at startup, including both temperature rise time and overshoot, and the steady-state disturbance rejection capabilities of each strategy. The test results showed that the MPC strategies controlled the sugar cooking process better than the traditional PID control method in regards to temperature rise time, temperature overshoot, and disturbance rejection based on feed rate disturbances. It was seen that the differences between the various MPC strategies was not significant relative to temperature overshoot and disturbance rejection. The PLC-based MPC strategy was comparable, but not superior, to the PC-based commercial MPC applications. However, this strategy has several benefits such as requiring no external hardware, software, and communications protocols, which may result in a less expensive implementation than the commercial MPC strategies. The PLC-based strategy is also easier and cheaper to maintain because it is developed on the existing, well-known control platform with existing tools

Aeronautical engineering: A continuing bibliography with indexes (supplement 315)

This bibliography lists 217 reports, articles, and other documents introduced into the NASA scientific and technical information system in Mar. 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics