An intelligent decision making and notification system based on a knowledge-enabled supervisory monitoring platform

This work describes a knowledge-based framework which is able to communicate online with process systems utilizing the existing infrastructure and provide knowledgeable actions to the operators. The proposed knowledge-enabled supervisory monitoring (KSM) platform combines components of existing standards such as ISA-95 in order to facilitate the data exchange between the automation system and the data repository. The functionalities of the developed platform are demonstrated using the requirements of an industrial control system of a continuous process and aim at the evaluation of the process and equipment performance based on predefined rules

Malfunction diagnosis in industrial process systems using data mining for knowledge discovery

The determination of abnormal behavior at process industries gains increasing interest as strict regulations and highly competitive operation conditions are regularly applied at the process systems. A synergetic approach in exploring the behavior of industrial processes is proposed, targeting at the discovery of patterns and implement fault detection (malfunction) diagnosis. The patterns are based on highly correlated time series. The concept is based on the fact that if independent time series are combined based on rules, we can extract scenarios of functional and non-functional situations so as to monitor hazardous procedures occurring in workplaces. The selected methods combine and apply actions on historically stored, experimental data from a chemical pilot plant, located at CERTH/CPERI. The implementation of the clustering and classification methods showed promising results of determining with great accuracy (97%) the potential abnormal situations

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

This work presents an optimum energy management framework, which is developed for integrated Polymer Electrolyte Membrane (PEM) fuel cell systems. The objective is to address in a centralized manner the control issues that arise during the operation of the fuel cell (FC) system and to monitor and evaluate the system’s performance at real time. More specifically the operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. To achieve these objectives a novel Model Predictive Control (MPC) strategy is developed and demonstrated. A semiempirical experimentally validated model is used which is able to capture the dynamic behaviour of the PEMFC. Furthermore, the MPC strategy was integrated in an industrial-grade automation system to demonstrate its applicability in realistic environment. The proposed framework relies on a novel nonlinear MPC (NMPC) formulation that uses a dynamic optimization method that recasts the multivariable control problem into a nonlinear programming problem using a warm-start initialization method and a search space reduction technique which is based on a piecewise affine approximation of the variable’s feasible space. The behaviour of the MPC framework is experimentally verified through the online deployment to a small-scale fully automated PEMFC unit. During the experimental scenarios the PEMFC system demonstrated excellent response in terms of computational effort and accuracy with respect to the control objectives

A Formal And Executable Model For Path Finding

One of the topics of research in the area of robotics is path finding i.e. to determine the discrete robot’s motion steps towards the accomplishment of a particular goal. The objective of the current project is to investigate the behavior and the operation of a hypothetical robot using the formal methodology of Coloured Petri Nets (CP-Nets). To this purpose a CP-Net model of the way the robot moves in a presumptive environment where a pilot-scale labyrinth exists is constructed. In this environment, the hypothetical robot is able to move, to change its direction, to avoid obstacles until it finds a preset goal. Results of the CP-Net model execution show how the prediction of robot's movements in its attempt to reach the goal in the environment with obstacles is performed and reveal a number of problems that are not intuitively obvious from the structure of the model.

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

This work presents an optimum energy management framework, which is developed for integrated Polymer Electrolyte Membrane (PEM) fuel cell systems. The objective is to address in a centralized manner the control issues that arise during the operation of the fuel cell (FC) system and to monitor and evaluate the system’s performance at real time. More specifically the operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. To achieve these objectives a novel Model Predictive Control (MPC) strategy is developed and demonstrated. A semiempirical experimentally validated model is used which is able to capture the dynamic behaviour of the PEMFC. Furthermore, the MPC strategy was integrated in an industrial-grade automation system to demonstrate its applicability in realistic environment. The proposed framework relies on a novel nonlinear MPC (NMPC) formulation that uses a dynamic optimization method that recasts the multivariable control problem into a nonlinear programming problem using a warm-start initialization method and a search space reduction technique which is based on a piecewise affine approximation of the variable’s feasible space. The behaviour of the MPC framework is experimentally verified through the online deployment to a small-scale fully automated PEMFC unit. During the experimental scenarios the PEMFC system demonstrated excellent response in terms of computational effort and accuracy with respect to the control objectives