Model predictive control of a mechanical pulp bleaching process

Abstract: In this paper we present and discuss all aspects of controlling a realworld delay-time system application, the pulp bleaching process at Irving Paper Ltd. The bleaching process was thoroughly studied and modelled. A delay-time estimator was designed to tackle the problem of the long variable delay time, which was considered the biggest challenge in this project. The model predictive control (MPC) strategy was chosen to control the bleaching process taking into account its constraints, which were handled by incorporating a state of the art optimization method, i.e., an interior point method, in the controller. The designed MPC controller was implemented in the Irving Paper mill, in order to test and demonstrate its performance and stability

A Multi-agent System for Integrated Control and Asset Management of Petroleum Production Facilities -- Part 2: Prototype Design Verification

This three-part paper thoroughly addresses the design and development of multi-agent system for asset management for the petroleum industry, which is crucial for profitable oil and gas facilities operations and maintenance. A research project was initiated to study the feasibility of an intelligent asset management system. Having proposed a conceptual model, architecture, and implementation plan for such a system in previous work [1], [2], [3], defined its autonomy, communications, and artificial intelligence (AI) requirements [4], [5], and initiated the preliminary design of a simple system prototype [?], we are extending the build of a system prototype and simulate it in real-time to validate its logical behavior in normal and abnormal process situations and analyze its performance. The second-part paper addresses the ICAM system prototype design verification and its logical behavior during sensor faults in the plant

Intelligent information, monitoring, and control technology for industrial process applications

Abnormal event management (AEM) in large manufacturing plants has evolved as a higher and increasingly vital function of process control. In this paper, an intelligent information management and control system is introduced. The different computational agents (i.e., modules) of the system are embodied in a three-layered cognitive hierarchy, which offers intelligent behavior at the system level, as well as at the level of specialized task agents. At the lower level, agents generate goal-seeking reactive behavior. Three different fault detection and isolation agents (i.e., three complementary techniques) are embedded to generate three different assessments and to enhance the fault isolation process. Other utility agents are also incorporated to address topics such as data reconciliation, process model identification and optimization. At the middle layer, agents enable decision making, planning, and deliberative behavior. Two case-based reasoning agents are incorporated; the first manages the system in normal operation, while the other handles faulty process situations. A meta-management agent at the highest level monitors and coordinates other agents so as to make the whole system performance more robust and coherent. 1

An intelligent multi agent system for integrated control and asset management of petroleum production facilities

Intelligent control and asset management for the petroleum industry is crucial for profitable oil and gas facilities operation and maintenance. A research program was initiated to study the feasibility of an intelligent asset management system for the offshore oil and gas industry in Atlantic Canada. The research program has achieved several milestones. The conceptual model of an automated asset management system, its architecture, and its behavioral model have been defined [1, 2]. Furthermore, an implementation plan for such system has been prepared, and the appropriate development tools have been chosen [3]. A system reactive agent structure was defined based on the MATLAB environment, and its communication requirements were analyzed and validated [31]. This paper builds on the previous work and proposes a general structure of the ICAM system intelligent supervisory agent and its software implementation. We also describe the software implementation using the G2 expert system development environment. Furthermore, we analyze and define the autonomy requirements of the reactive agents of such system. 1