Design and Implementation of MPC for Energy Optimization of Boiler in Batch Distillation Column

A competency in the industrial world depends on several aspects, that is cost, delivery, flexibility, and quality. The smart industrial system / Smart Manufacturing System (SMS) tries to improve those aspects using the latest technology that encourages the use of digital information widely and quickly in industrial systems. Development of SM in industry 4.0, pushing the change of industrial pyramids into CyberPhysical System (CPS). CPS has begun to be applied widely in process industries nowadays, i.e., distillation process industry. In distillation process, boiler has the important role to separate 2 different components using the difference of its boiling point. In this paper, the alcohol distillation plant is used to purify 30% of alcohol solution. The modelling of boiler, simulation, and implementation of boiler control system are presented to get the desired temperature. The temperature reference is roughly 85 oC. Predictive Model Controller (MPC) and Kalman Filter is proposed to control the temperature of boiler by adjusting the PWM of on-off time and to deal with the disturbance and sensor noise. The IAE, ISE, and ITAE is analyzed to obtain the error of control system and energy usage per operation is also calculated to find out the effect of MPC controller in energy optimizatio

Chemical process control : present status and future needs ; the view from European industry

Not only in Europe, chemical process control is characterized by a broad invasion of distributed control systems into chemical plants. The information integration from process control up to business management is a great challenge of today which follows from the overall computerization of production. Most of the recent progress in process automation results from the application of computer science paradigms to control systems, and of advanced developments in field instrumentation. Despite these advances and the considerable progress made in process control theory, there is only limited acceptance and application of modern advanced process control methodologies in industrial practice. This paper is an attempt to summarize the European discussion on the reasons for these facts

Framework for operability assessment of production facilities: an application to a primary unit of a crude oil refinery

This work focuses on the development of a methodology for the optimization, control and operability of both existing and new production facilities through an integrated environment of different technologies like process simulation, optimization and control systems. Such an integrated environment not only creates opportunities for op¬erational decision making but also serves as training tool for the novice engineers. It enables them to apply engineering expertise to solve challenges unique to the process industries in a safe and virtual environment and also assist them to get familiarize with the existing control systems and to understand the fundamentals of the plant operation. The model-based methodology proposed in this work, starts with the implementation of first principle models for the process units on consideration. The process model is the core of the methodology. The state of art simulation technologies have been used to model the plant for both steady state and dynamic state conditions. The models are validated against the plant operating data to evaluate the reliability of the models. Then it is followed by rigorously posing a multi-optimization problem. In addition to the basic economic variables such as raw materials and operating costs, the so-called “triple-bottom-line” variables related with sustainable and environmental costs are incorporated into the objective function. The methodologies of Life Cycle Assessment (LCA) and Environmental Damage Assessment (EDA) are applied within the optimization problem. Subsequently the controllability of the plant for the optimum state of conditions is evaluated using the dynamic state simulations. Advanced supervisory control strategies like the Model Predictive Control (MPC) are also implemented above the basic regulatory control. Finally, the methodology is extended further to develop training simulator by integrating the simulation case study to the existing Distributed Control System (DCS). To demonstrate the effectiveness of the proposed methodology, an industrial case study of the primary unit of the crude oil refinery and a laboratory scale packed distillation unit is thoroughly investigated. The presented methodology is a promising approach for the operability study and optimization of production facilities and can be extended further for an intelligent and fully-supportable decision making

Fuzzy model predictive control. Complexity reduction by functional principal component analysis

En el Control Predictivo basado en Modelo, el controlador ejecuta una optimización en tiempo real para obtener la mejor solución para la acción de control. Un problema de optimización se resuelve para identificar la mejor acción de control que minimiza una función de coste relacionada con las predicciones de proceso. Debido a la carga computacional de los algoritmos, el control predictivo sujeto a restricciones, no es adecuado para funcionar en cualquier plataforma de hardware. Las técnicas de control predictivo son bien conocidos en la industria de proceso durante décadas. Es cada vez más atractiva la aplicación de técnicas de control avanzadas basadas en modelos a otros muchos campos tales como la automatización de edificios, los teléfonos inteligentes, redes de sensores inalámbricos, etc., donde las plataformas de hardware nunca se han conocido por tener una elevada potencia de cálculo. El objetivo principal de esta tesis es establecer una metodología para reducir la complejidad de cálculo al aplicar control predictivo basado en modelos no lineales sujetos a restricciones, utilizando como plataforma, sistemas de hardware de baja potencia de cálculo, permitiendo una implementación basado en estándares de la industria. La metodología se basa en la aplicación del análisis de componentes principales funcionales, proporcionando un enfoque matemáticamente elegante para reducir la complejidad de los sistemas basados en reglas, como los sistemas borrosos y los sistemas lineales a trozos. Lo que permite reducir la carga computacional en el control predictivo basado en modelos, sujetos o no a restricciones. La idea de utilizar sistemas de inferencia borrosos, además de permitir el modelado de sistemas no lineales o complejos, dota de una estructura formal que permite la implementación de la técnica de reducción de la complejidad mencionada anteriormente. En esta tesis, además de las contribuciones teóricas, se describe el trabajo realizado con plantas reales en los que se han llevado a cabo tareas de modelado y control borroso. Uno de los objetivos a cubrir en el período de la investigación y el desarrollo de la tesis ha sido la experimentación con sistemas borrosos, su simplificación y aplicación a sistemas industriales. La tesis proporciona un marco de conocimiento práctico, basado en la experiencia.In Model-based Predictive Control, the controller runs a real-time optimisation to obtain the best solution for the control action. An optimisation problem is solved to identify the best control action that minimises a cost function related to the process predictions. Due to the computational load of the algorithms, predictive control subject to restric- tions is not suitable to run on any hardware platform. Predictive control techniques have been well known in the process industry for decades. The application of advanced control techniques based on models is becoming increasingly attractive in other fields such as building automation, smart phones, wireless sensor networks, etc., as the hardware platforms have never been known to have high computing power. The main purpose of this thesis is to establish a methodology to reduce the computational complexity of applying nonlinear model based predictive control systems subject to constraints, using as a platform hardware systems with low computational power, allowing a realistic implementation based on industry standards. The methodology is based on applying the functional principal component analysis, providing a mathematically elegant approach to reduce the complexity of rule-based systems, like fuzzy and piece wise affine systems, allowing the reduction of the computational load on modelbased predictive control systems, subject or not subject to constraints. The idea of using fuzzy inference systems, in addition to allowing nonlinear or complex systems modelling, endows a formal structure which enables implementation of the aforementioned complexity reduction technique. This thesis, in addition to theoretical contributions, describes the work done with real plants on which tasks of modeling and fuzzy control have been carried out. One of the objectives to be covered for the period of research and development of the thesis has been training with fuzzy systems and their simplification and application to industrial systems. The thesis provides a practical knowledge framework, based on experience

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

Control and Optimization of Batch Chemical Processes

A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent “time” variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches

Suurten datamäärien hallinta prosessiteollisuudessa

The idea of Internet of Things (IoT) is to connect all the devices into one network and to enable interoperability between them. Interoperability benefits also the process industry when the control devices and software can interoperate with management software. One part of the industrial IoT is being able to efficiently analyze the data from the field devices so that for example predictive maintenance can be achieved. Information modelling is needed to enable communication between the different software and to make analyzing data easier. This thesis examines the state of the IoT and the benefits of information modelling. The aim is to find the information modelling standard most suitable for the process industry and to figure out how standard conforming information models are created. The literature part of this thesis studies the current state and the future of IoT. The focus is especially on the possibilities it brings for the oil and gas industry. A broad collection of information modelling standards is introduced. According to the comparison made, OPC UA was selected in this work as the most suitable standard for the needs of process industry. In the experimental part the information modelling process is introduced and three OPC UA modelling tools are examined. Instructions for information modelling with OPC UA were created. An OPC UA standard conforming information model of a distillation column was created to be used to configure a soft sensor. The model was validated using expert knowledge. The model was also successfully connected to a data source that was in this case a DCS emulator.Esineiden internetin ajatuksena on kytkeä kaikki laitteet samaan verkkoon ja mahdollistaa niiden välinen yhteensopivuus. Myös prosessiteollisuudessa on hyötyä yhteensopivuudesta, kun säätölaitteet ja ohjausjärjestelmät voivat kommunikoida hallintojärjestelmien kanssa. Teollisessa esineiden internetissä kenttälaitteiden tuottamaa data pystytään analysoimaan tehokkaasti siten, että esimerkiksi ennakoiva huolto on mahdollista. Tietomalleja tarvitaan laitteiden välisen kommunikaation mahdollistamiseksi ja tiedon analysoinnin helpottamiseksi. Tämä diplomityö käsittelee esineiden internetin tilaa sekä tietomallinnuksella saavutettavia hyötyjä. Tavoitteena on löytää prosessiteollisuuteen sopivin tietomallinnusstandardi sekä selvittää, miten valitun standardin mukaisia tietomalleja laaditaan. Kirjallisuusosassa selvitellään esineiden internetin nykytila sekä tulevaisuudennäkymät. Erityisest keskitytään esineiden internetin öljy- ja kaasuteollisuudelle tuomiin mahdollisuuksiin. Työssä esitellään laaja kokoelma tietomallinnusstandardeja. Tehdyn vertailun jälkeen OPC UA valittiin tässä työssä prosessiteollisuuden käyttötarkoitukisiin sopivimmaksi standardiksi. Soveltavassa osassa esitellään tietomallinnusprosessi sekä tutustutaan kolmeen erilaiseen OPC UA tietomallinnustyökaluun. Tietomallintamisesta OPC UA -standardin avulla laadittiin ohjeet. Työssä laadittiin OPC UA:n mukainen tietomalli tislauskolonnista virtuaalisen säätimen konfigurointikäyttöön. Laaditun mallin toimivuutta arvioitiin asiantuntijoiden avulla. Malli kiinnitettiin onnistuneesti tietolähteeseen, joka tässä tapauksessa oli DCS emulaattori

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering