Although the proportional-integral-derivative (PID) controller has only three parameters, it is not easy, without a systematic procedure, to find good values (settings) for them. In fact, a visit to a process plant will usually show that a large number of the PID controllers are poorly tuned. In general, excellent results are obtained if one is willing to invest some time and take a systematic approach and invest some time. The following two-step procedure works well, at least for typical stable processes encountered in the process industry: Step 1. Obtain a first- or second-order plus delay model. Step 2. Derive model-based SIMC controller settings. With the SIMC method, PI-settings result if we start from a first-order model, whereas PID-settings result from a second-order model. The SIMC method is based on classical ideas presented earlier by Ziegler and Nichols (1942), the IMC PID-tuning paper by Rivera et al. (1986), and the closely related direct synthesis tuning rules in the book by Smith and Corripio (1985). The Ziegler-Nichols settings result in a very good disturbance response for integrating processes, but are otherwise known to result in rather aggressive settings (Tyreus and Luyben 1992) (Astrom and Hagglund 1995), and also give poor performance for processes with a dominant delay. On the other hand, the analytically derived IMC-settings of Rivera et al. (1986) are known to result in poor disturbance response for integrating processes (Chien and Fruehauf 1990), (Horn et al. 1996), but are robust and generally give very good responses for set point changes. The SIMC tuning rule works well for both integrating and pure time delay processes, and for both set points and load disturbances. It is actually close to the optimum as can be seen by evaluating the Pareto-optimality of the SIMC method with respect to he conflicting objectives of performance and robustness. The results with PID control are generally also better than with the model-based Smith Predictor, even with processes with large time delays. This is surprising, and it shows that if one puts enough effort into the PID tuning then there is little benefit in considering more complex controllers, including MPC.

Sigurd Skogestad is a professor in chemical engineering at the Norwegian University of Science and Technology (NTNU) in Trondheim. Born in Norway in 1955, he received the Siv.Ing. degree (M.S.) in chemical engineering at NTNU in in 1978. After finishing his military service at the Norwegian Defence Research Institute, he worked from 1980 to 1983 with Norsk Hydro in the areas of process design and simulation at their Research Center in Porsgrunn, Norway. Moving to the US and working 3.5 years under the guidance of Manfred Morari, he received the Ph.D. degree from the California Institute of Technology in 1987. He has been a full professor at NTNU since 1987. During the period 1999 to 2009 he was Head of Department of Chemical Engineering ( Kjemisk prosessteknologi ). He was at sabattical leave at the University of California at Berkeley in 1994-95, and at the University of California at Santa Barbara in 2001-02. The author of about 200 international journal publications and 200 conference publications, he is the principal author together with Ian Postlethwaite of the book "Multivariable feedback control" published by Wiley in 1996 (first edition) and 2005 (second edition). Dr. Skogestad was awarded "Innstilling to the King" for his Siv.Ing. degree in 1979, a Fullbright fellowship in 1983, received the Ted Peterson Award from AIChE in 1989, the George S. Axelby Outstanding Paper Award from IEEE in 1990, the O. Hugo Schuck Best Paper Award from the American Automatic Control Council in 1992, and the Best Paper of the Year 2004 Award from Computers and Chemical Engineering. He was an Editor of Automatica during the period 1996-2002 and is member of the IFAC Technical Board for the period 2008 to 2014. He is a Fellow of the American Institute of Chemical Engineers (2012) and was elected into the Process Control Hall of Fame in 2011. Professor Skogestad has graduated 34 PhD candidates (1990-2012). He presently has a group of about 6 Ph.D. students and is the Head of PROST which is the strong point center in process systems engineering in Trondheim and involves about 50 people in various departments. The goal of his research is to develop simple yet rigorous methods to solve problems of engineering significance. Research interests include the use of feedback as a tool to (1) reduce uncertainty (including robust control), (2) change the system dynamics (including stabilization), and (3) generally make the system more well-behaved (including self-optimizing control). Other interests include limitations on performance in linear systems, control structure design and plantwide control, interactions between process design and control, and distillation column design, control and dynamics. His other main interests are mountain skiing (cross country), orienteering (running around with a map) and grouse hunting.

Sustainability commonly means the protection of environmental resources and economic growth with not only the consideration of the social, economic and environmental influence but also the enhancement of human health and life. The care of supply chain related to feedstock, transportation and production has to be essential for achieving economic stability and sustainable development. In this respect, diversity of energy resources is a significant issue for the synthesis and optimization of energy process. The renewable or synthetic energy resources stand out as a solution of conflictions not only between limited energy resources and increasing energy demand but also between increasing energy consumption and air pollution related to global warming. Recently synthetic energy sources which have the benefits such as environmental friendliness, high energy density, and potential for commercialization have been in the lead among alternatives to petroleum based energy. This paper deals with dimethyl ether (DME) production process which is one of the synthetic energy challenges. DME is sulfur-free and near-zero aromatics synthetic fuel, considered as an excellent substitute for conventional diesel and liquefied petroleum gas (LPG). As DME can be produced from synthesis gas (syngas), it is possible to be synthesized from natural gas, coal, biomass, and/or coal seam. State-of-the-art technologies for DME production from biomass feedstock and a superstructure representation were investigated. For carrying out a comprehensive design study, a mathematical representation of each process and mixed-integer logic conditions are associated with the selection of technologies and connectivity restrictions. From this work, the process involved other energy resources which have various types of feedstock, and production technologies can be equally applied so that the synthesis of energy process promises sustainability. Sustainability, which must be considered for the future, can be expressed as the following equation (1).

(1)

En Sup Yoon has been very active professor in Seoul National University at chemical engineering department, since 1983. Currently, he is responsible for many organizations. For instance, he has been adviser in Korean Society of Hazard Mitigation, chair for World Conference of Safety of Oil and Gas Industry, president in Korean association of professional safety engineers, and adviser in both System & safety Plus technology Corporation and Daewoo shipbuilding & Marine Engineering Co., Ltd. His outstanding research and contribution in chemical process has made won many rewards. He was the recipient of the Outstanding Paper Award of 2010 from the society of Chemical Engineers, Honorary Award from Seoul National University in 2003, Award for Monthly Best Scientist from Ministry of Science & Technology in 1998 and etc. He was not only a good researcher but also devoted teacher. His passion towards his student had made 131 master students and 67 Ph.D students who are now working in many different areas and showing their leadership in the world. The main area of his current research is chemical process safety minimizing the human risk and the sustainability in chemical industry.

The aim of this talk is to share the idea of a real-time optimization (RTO) algorithm tailored for nonlinear model predictive control (NMPC) and to introduce some cutting edge applications. Model predictive control (MPC) is one of the most successful control techniques in industrial processes. At each sampling time of MPC, the response of a plant over a finite future is predicted by using a model of the plant and is optimized by minimizing a given performance index subject to some constraints. MPC can handle various types of control objectives and constraints and achieve the best possible performance as long as the optimal control problem over the finite future can be solved at each sampling time in real time, which is an appealing feature of MPC as a general framework of feedback control for nonlinear systems and for realizing any sort of intelligent systems. Although NMPC, MPC for nonlinear systems, had long been considered unrealistic owing to the heavy computational burden for optimization, advances in computation methods and computers have been expanding the frontiers of NMPC. In particular, it is possible to develop an efficient RTO algorithm on the basis of the assumption that the sampling period is sufficiently short. The RTO algorithm is obtained as an initial-value problem of an ordinary differential equation for an unknown quantity to be optimized. That is, the RTO algorithm can be viewed as a dynamical system to generate a time-dependent optimal solution with no iterative search. Through the use of the non-iterative RTO algorithm, NMPC with a sampling period on the order of milliseconds is becoming more and more common. Application examples of NMPC nowadays include a robotic manipulator, a radio-controlled hovercraft, the autopilot of a ship, path generation for an automobile, and a steel making process. NMPC of distributed parameter systems such as thermofluid systems is one of the ongoing research challenges. The idea of the RTO algorithm can also be adopted in differential games, estimation, and adaptive control with moving horizons. Moreover, a program of the RTO algorithm for a particular application can be automatically generated by such symbolic computation languages as Maple and Mathematica, which is quite useful for dealing with complex nonlinear systems.

Toshiyuki Ohtsuka was born in 1967 in Tokyo, Japan. He received his Bachelor, Master and Doctor Degrees in Engineering from Tokyo Metropolitan Institute of Technology, Japan, in 1990, 1992 and 1995, respectively. From 1995 to 1999, he worked as an Assistant Professor at the Institute of Engineering Mechanics, the University of Tsukuba. In 1999, he joined Osaka University as an Associate Professor at the Department of Mechanical Engineering, the Graduate School of Engineering, and he was a Professor at the Department of Systems Innovation, the Graduate School of Engineering Science from 2007 to 2013. In 2013, he joined Kyoto University as a Professor at the Department of Systems Science, the Graduate School of Informatics. His research interests include nonlinear control theory and real-time optimization with applications to mechanical systems and environmental systems. He is a member of SICE, IEEE, ISCIE, AIAA, JSASS, and JSME. He was the recipient of the SICE Control Division Pioneer Award in 2006, SICE Best Paper Awards in 2004 and 2013, SICE Best Writing Award in 2012 for his book, “Introduction to Nonlinear Optimal Control” (in Japanese), among other academic awards.

It is now common to have archival history of thousands of sensors sampled every second over long time periods. Yet we frequently have process engineers complain: “….We are drowning in data but starving for information…”. How can these rich data sets be put to use? This talk will address the issue of information and knowledge extraction from data with emphasis on process and performance monitoring including fault detection and isolation. Most of the major plant, factory, process, equipment and tool disruptions are avoidable, and yet preventable and predictive data analytics for fault detection and diagnosis are not the norm in most industries. It is not uncommon to see simple and preventable faults disrupt the operation of an entire integrated manufacturing facility. For example, faults such as malfunctioning sensors or actuators, inoperative alarm systems, poor controller tuning or configuration can render the most sophisticated control systems useless. Such disruptions can cost in the excess of $1 million per day and on the average they rob the plant of 7% of its annual capacity. Process data analytic methods rely on the notion of sensor fusion whereby data from many sensors or units are combined with process information, such as physical connectivity of process units, to give a holistic picture of health of an integrated plant. Such methods are at a stage where these strategies are being implemented for off-line and on-line deployment. Typical analytic methods require the execution of following steps: data quality assessment, outlier detection, noise filtering, data segmentation followed by process and performance monitoring including root cause detection of faults. For efficient and informative analytics, data analysis is ideally carried out in the temporal as well as spectral domains, on a multitude and NOT singular sensor signals to detect process abnormality. This presentation will discuss the field of process data analytics via industrial case studies. The case studies involve detection and investigation of root cause(s) of plant-wide oscillations that require the determination of process topology from process data, using Granger causality and transfer entropy methods, to explain the propagation of plant-wide disturbances.

Sirish L. Shah has been with the University of Alberta since 1978, where he is currently Professor of Chemical and Materials Engineering and held the NSERC -Matrikon-Suncor-iCORE Senior Industrial Research Chair in Computer Process Control from 1999 to 2012. He was the recipient of the Albright & Wilson Americas Award of the Canadian Society for Chemical Engineering (CSChE) in recognition of distinguished contributions to chemical engineering in 1989, the Killam Professor in 2003 and the D.G. Fisher Award of the CSChE for significant contributions in the field of systems and control in 2006. He has held visiting appointments at Oxford University and Balliol College as a SERC fellow , Kumamoto University (Japan) as a senior research fellow of the Japan Society for the Promotion of Science (JSPS) , the University of Newcastle, Australia, IIT-Madras, India and the National University of Singapore. The main area of his current research is process and performance monitoring, system identification and design and implementation of softsensors. He has co-authored two books, the first titled, Performance Assessment of Control Loops: Theory and Applications, and a recent book titled ‘Diagnosis of Process Nonlinearities and Valve Stiction: Data Driven Approaches'.