Evaluation of refinery control systems

Towards improved automation for desalination processes, Part I: Advanced control

Undesirable environmental effects of industrial development can be significantly reduced by advanced process control strategies. Although criteria of particular process control problems are often mutually contradictive, a reasonable compromise solution reflecting manufacturing and environmental considerations can be found. This problem presents a challenging task due to the high complexity of typical manufacturing processes. Development of the theoretical fundamentals for the solution of relevant mathematical modeling, control, information, software development and implementation problems is proposed. The study utilizes the principle of binarity formulated for variable-structure systems, Rasumihin's principle (1988) and vector Lyapunov's functions applied to robust multipurpose automatic control systems.

Advanced Process Control System Design for a Refinery: A Case Study

The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)‐based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5‐MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.

Publisher Summary Automatic control has long been a significant feature of the process industries, but only recently has there been any attempt to treat process control problems quantitatively. This chapter discusses automatic process control in the chemical engineering picture. It identifies and evaluates the possibilities of intelligent development of the field. The important broad concepts are presented in a general description of the nature of control. These concepts are applied to the solution of typical process control problems in an elucidation of available techniques. The theoretical developments in the field of servomechanisms and governors are generally applicable to process control problems. A great variety of measuring devices for determining the chemical composition of process flow streams is now available, and many of these instruments are being incorporated in automatic control systems. The developments that can treat control problems are dynamic characterization of processes from normal operating methods of attacking problems involving nonlinear systems; and proved techniques for sampled data systems. The analytic control problem deals with the appraisal of existing control systems. On the other hand, the synthetic problem is the design of the whole control system, including in its broadest implication the design of process, as well as the specification of, control instruments. Before the synthetic problem can be tackled intelligently, the criteria of satisfactory control must be identified. While doing process control, only a few types of control action, that is, control modes are important, namely: on-off or two-position control; proportional control; integral control or automatic reset; and derivative or rate action.

The semiconductor manufacturing process is very complicated. The operations are performed on different tools, supplied by different vendors, forcing many semiconductor manufacturers to adopt computer integrated manufacturing (CIM). The challenge in integrating semiconductor-manufacturing environment comes from connectivity and configurability. The CIM strategy for semiconductor fab described in this paper, is developed using building blocks. The building blocks can be seen as a collection of resources-both physical and functional, which can be used individually. These building blocks interact within a loosely coupled framework for task execution, thus allowing the flexibility of adding/changing functionality with minimal impact on the existing system. This is demonstrated by adding various optional building blocks like real-time data acquisition and advanced process control blocks to the CIM application. The base application framework has been used to integrate more than 250 semiconductor manufacturing tools from various vendors at TI semiconductor fab.

In the implementation of computer integrated manufacturing (CIM), there are varying degrees of integration. Within the semiconductor wafer manufacturing industry, only a few companies have installed a comprehensive and fully automated CIM system in their facilities. Thus, compared to other industries, wafer fabs continue to require significant numbers of production personnel. This makes work-in-process (WIP) control and yield problems more complex and difficult. This paper describes the key points of a state-of-the-art automation system, called FABCON, that was developed and is being implemented in an ASIC wafer fabrication facility (fab) in Japan. This system was created through the collaboration of both Japanese and American companies. Along with the traditional CIM functions of automatic lot tracking, recipe setting, and process data collection, this system also creates the production schedule automatically, and controls the transport of almost all wafer carriers throughout the fab by robots. Specifically, this paper discusses a distributed fab control system based on ISA standard 386 and 486 based PC's, a dynamic shop floor scheduler/dispatcher, wafer carrier transportation robotics, feedback/feedforward process control, and standardization of equipment interfaces. In addition, this paper describes some of the experiences in the course of development and implementation of the system. >

The increasing requirements for better quality with smaller variations are often difficult to fulfill in pulping plants. In chemi-thermo mechanical pulping (CTMP), the non-uniformity in the chemical treatment and the changes in the refiner's energy consumption often cause variations in pulp properties. The CTMP process has strong interactions between process variables and therefore necessitates decoupling of interactions in the refining control system. Together with the high variability in the process dynamics, lack of direct and reliable measurements makes it difficult to control the process. Consistency of the blow-line pulp is not always measured using an on-line sensor. If high consistency sensors are used, also redundant measurements or soft sensors may need to be developed for feedback control purposes. Pulp quality variables such as freeness and shive content are often analyzed infrequently and/or there is a long dead time and time constant between the refiner's blow line and the on-line analyzer's sensor. In this paper, pulp properties of CTMP refining are modeled and a new two-level quality control system is proposed. The nonlinear quality models are verified by simulation and compared with measurements from an existing on-line pulp quality analyzer. The two-level quality control system uses the process models for stabilizing the refiner conditions (pulp consistency and specific energy consumption) and for optimizing the process operation and the pulp quality (freeness). The stabilizing controllers are designed using multivariable Hammerstein and cascade control structures. The optimizing controller calculates the setpoint for the stabilizing controller by using the inverse of the nonlinear multivariable quality model. A simple scheme for adapting the freeness model based on the on-line measurements is presented. The performance of the control system is illustrated in simulations.

An advanced digital control and monitoring system for turbomachinery is introduced. Important aspects for the design of anti-surge control systems, such as the influence of the controller program execution time on the performance, are investigated. The influence of the gas composition (molecular weight) on the compressor surge line is demonstrated. Control laws are discussed. The design basis of an ultra-high speed anti-surge and speed control system is shown and the data-logging features are demonstrated on the log of a compressor surge. The paper closes describing some dynamic process simulation being performed using the controller hardware and software for process simulations.

High share of energy consumption in buildings and subsequent increase in greenhouse gas emissions along with stricter legislations have motivated researchers to look for sustainable solutions in order to reduce energy consumption by using alternative renewable energy resources and improving the efficiency in this sector. Today, the smart building and socially resilient city concepts have been introduced where building automation technologies are implemented to manage and control the energy generation/consumption/storage. Building automation and control systems can be roughly classified into traditional and advanced control strategies. Traditional strategies are not a viable choice for more sophisticated features required in smart buildings. The main focus of this paper is to review advanced control strategies and their impact on buildings and technical systems with respect to energy/cost saving. These strategies should be predictive/responsive/adaptive against weather, user, grid and thermal mass. In this context, special attention is paid to model predictive control and adaptive control strategies. Although model predictive control is the most common type used in buildings, it is not well suited for systems consisting of uncertainties and unpredictable data. Thus, adaptive predictive control strategies are being developed to address these shortcomings. Despite great progress in this field, the quantified results of these strategies reported in literature showed a high level of inconsistency. This is due to the application of different control modes, various boundary conditions, hypotheses, fields of application, and type of energy consumption in different studies. Thus, this review assesses the implementations and configurations of advanced control solutions and highlights research gaps in this field that need further investigations.

Dynamic simulation and control studies of CPR1000 reactor core with advanced MSHIM control strategy

Advanced control strategies are designed for an activated sludge‐based wastewater treatment plant (WWTP) consisting of two anoxic, three aerobic reactors and secondary settler by considering the influent data from an existing WWTP. Four different control strategies (PI/FPI/MPC/FLC) are designed and a systematic procedure is developed to implement these control strategies. The plant performance is evaluated using operational cost index (OCI) and effluent quality index (EQI) and the controller performance is assessed based on integral of squared error. The EQI and OCI for open loop are reported as 4469 and 12 743 respectively. Whereas, when advanced controllers are implemented, a significant decrease of 19.89% and 5.24% in EQI and OCI is observed for MPC and a decrease of 20.9% and 4.63% in EQI and OCI is observed for fuzzy controller. The outcomes of this research help the wastewater utilities to adopt advanced control strategies for enhanced operation and decision‐making.

Starting from a previous technical and economical feasibility study, this paper analyzes the integration of Renewable Energy Sources into an existing territory with specified features of the natural landscape and of the built environment. The work puts into evidence that territories development must use suitable tools and rules based on integrated knowledge, since technical feasibility studies do not assess the sustainability of the proposed infrastructures within the built environment and landscape. The studied system is in the island of Pantelleria situated between Sicily and northern Africa.KeywordsSustainability of microgridsEnvironmental impact assessmentEnvironmental SustainabilityDevelopment of renewable energy systems

General Electric, the National Renewable Energy Laboratory (NREL), the University of Massachusetts Amherst (UMass), and Glosten have recently completed a US Department of Energy (DOE)-funded research program to study technologies for mitigating loads on floating offshore wind turbines through the use of advanced turbine controls and tuned mass dampers (TMDs). The analysis was based upon the Glosten PelaStar tension leg platform (TLP) with GE Haliade 150 turbine, a system developed in a previous front end engineering design (FEED) study funded by the Energy Technology Institute (ETI) in the UK. The platform was designed for the WaveHub wave energy research site, with a mean water depth of 59-m. Loads were analyzed by running time-domain simulations in four 50-year return period (50-YRP) ultimate load state (ULS) conditions and 77 fatigue load state (FLS) environmental conditions. In 50-YRP conditions advanced controls are not active. The influence of TMDs on ULS loads have been reported previously (Park et al. [2]). In FLS conditions advanced controls and TMDs afford dramatic reductions in fatigue damage, offering the potential of significant savings in tower structural requirements. Simulations in turbine idling conditions were run in OrcaFlex, and simulations in operating conditions were run in FAST. Simulations were run with a baseline turbine controller, representative of the current state of the art, and an advanced controller developed by NREL to use collective and individual blade pitch control to maintain rotor speed and reduce tower loads. UMass developed a number of TMD types, with varying system configurations, including passive nonlinear dampers and semi-actively controlled dampers with an inverse velocity groundhook control algorithm. Loads and accelerations in FLS conditions were evaluated on the basis of damage equivalent loads (DELs), and fatigue damage was computed by Miner’s summations of stress cycles at the tower base. To study sensitivity to water depth, loads were analyzed at both the 59-m WaveHub depth and a more commercially realistic depth of 100 m. TMDs reduce fatigue damage at the tower-column interface flange by up to 52% in 59-m water depth and up to 28% in 100 m water depth. Advanced controls reduce fatigue damage at the tower-column flange by up to 22% in 59-m water depth and up to 40% in 100 m water depth. The most effective load-mitigation strategy is combining advanced controls with TMDs. This strategy affords a 71% reduction in fatigue damage in both 59-m and 100-m water depths.

The paper explores the standard advanced control elements commonly used in industry for designing advanced control systems. These elements include cascade, ratio, feedforward, decoupling, selectors, split range, and more, collectively referred to as “advanced regulatory control” (ARC). Numerous examples are provided, with a particular focus on process control. The paper emphasizes the shortcomings of model-based optimization methods, such as model predictive control (MPC), and challenges the view that MPC can solve all control problems, while ARC solutions are outdated, ad-hoc and difficult to understand. On the contrary, decomposing the control systems into simple ARC elements is very powerful and allows for designing control systems for complex processes with only limited information. With the knowledge of the control elements presented in the paper, readers should be able to understand most industrial ARC solutions and propose alternatives and improvements. Furthermore, the paper calls for the academic community to enhance the teaching of ARC methods and prioritize research efforts in developing theory and improving design method.