Rui Neves-Silva

Process control based on PCA models

In this paper an approach to design controllers based on principal components analysis (PCA) models is presented. Closed-loop control can be formulated and implemented within the reduced space defined by a PCA model. This PCA controller, results in an integral controller, which can be used as an inferential controller when a measurement of a primary variable is not available. The main contributions of the paper are: a) the incorporation of an adjustable gain on the classical PCA controller; b) the proposal of a set of tuning rules; c) the performance evaluation of this new controller, in nominal operation conditions and in faulty situations. Some experimental results, obtained with the three tank benchmark (European COSY project), are presented.

Design of Adaptive PCA Controllers for SISO Systems

Abstract In this paper an approach to design adaptive controllers, for SISO systems, based on principal components analysis (PCA models) is presented. Closed-loop control can be formulated and implemented within the reduced space defined by a PCA model. This PCA controller, results in an integral controller, which can be used as an inferential controller for nonlinear systems. The main contribution of the paper is the adaptive mechanism based on online recursive PCA, using eigenvector perturbation, for process control applications. Some experimental results, obtained with a nonlinear system (the three tank benchmark) are presented, showing the controller performance.

A SERVICE ORIENTED FRAMEWORK FOR CONTEXT AWARE KNOWLEDGE ENHANCING

Abstract The understanding and applying of knowledge can be difficult if it is isolated from the context under which the knowledge is generated and managed. Despite the importance of contextual information, there has been limited support for utilizing context in existing knowledge management solutions. This paper presents a set of integrated services for context aware enhancing of knowledge and knowledge management: monitoring services monitor human computer interaction to implicitly capture their interactions with the explicit or implicit data; context extraction services extract activity centric knowledge context from the monitoring data, as well as support users to manage their knowledge-based activities; knowledge enhancing services enhance knowledge and knowledge management by taking into consideration the context and topic of the knowledge-based activity. The services are provided in a loosely coupled service oriented framework which can be easily integrated with existing information and knowledge management systems to maximize its usability and extendibility.

A decision support system for energy-efficiency investments on building renovations

When intending to renovate an existing building, with energy efficiency and greenhouse gas emissions in mind, a building owner is always questioning himself if the available investment resources are being directed to an effective return and if there are ways to improve this return. In addition, it is quite difficult to identify all the criteria to be considered in such decision and how to structure it in a formal and systematic process that involves all necessary stakeholders. This paper presents an innovative approach to support decision-making when choosing energy-efficiency investments on building renovations. The proposed approach uses prediction based on model consumption to propose alternative energetic scenarios and the Analytic Hierarchy Process to select the best one.

Ambient Intelligence Based System for Life-cycle Management of Complex Manufacturing and Assembly Lines

The key idea of the work presented is to explore how a combination of advanced Ambient Intelligence (AmI) and Knowledge Management (KM) technologies can be used to assure a sustainable and safe use of Manufacturing and Assembly Lines (MAL) and their infrastructure over their life-cycle. The specific intention is to reduce the whole service-life operational costs and impact of MAL, providing new ways to monitor on-line Life-Cycle Parameters (LCP) of MAL and improved services to support MAL throughout its whole life-cycle. The paper presents a new system for effective life cycle management of complex MAL, including computation of LCP based on information obtained from AmI systems within MAL, and delivery of improved services around MAL.

Risk Assessment to Support Decision on Complex Manufacturing and Assembly Lines

The key idea of this work, developed under the scope of InLife project, is to use the risk assessment to decide on which service to fire from the ones available in the life-cycle managing system software platform. The risk of a specific situation affecting a manufacturing and assembly line (MAL), characterized by the symptoms, is estimated from the information stored on the system concerning probability of occurrence and its consequences. It is expected that this knowledge grows along the life-cycle of a MAL. Then for each level of risk, predetermined by the industrial users, the adequate service will be called for promptly reaction in solving the problem or avoiding critical situations.

Supporting Distributed Collaborative Work in Manufacturing Industry

Manufacturing companies face a great need to improve competitiveness and strengthen market position. This pressure demands a strong collaboration from all actors involved in the product or production value chain. However, manufacturing industry is still missing the necessary support, in methodologies and tools, to achieve efficient collaboration strategies. This paper introduces an innovative approach to support collaboration in assembly and manufacturing systems, being developed in the scope of the European project InAmI. The main idea of the approach is to combine agent, ambient intelligence and semantic-based knowledge management technologies to provide the most appropriate use of all knowledge within extended enterprises, to support collaboration. The InAmI innovative approach comprehends a methodology and a platform for the efficient creation and maintenance of collaborative services

Distributed MPC for Thermal Comfort in Buildings with Dynamically Coupled Zones and Limited Energy Resources

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited energy resource using a demand-side management approach. The building divisions are modeled using an electro-thermal modular scheme. For control purposes, this modular scheme allows an easy modeling of buildings with different plans where adjacent areas can thermally interact. The control objective of each subsystem is to minimize the energy cost while maintaining the indoor temperature in the selected comfort bounds. In a distributed coordinated environment, the control uses multiple dynamically coupled agents (one for each subsystem/zone) aiming to achieve satisfaction of available energy coupling constraints. The system is simulated with two zones in a distributed environment.

Risk based decision support system for life cycle management of industrial plants

Industrial plants tends to be immersed in a distressing environment and the decision making process can be hindered by some blocking conditions. The key idea of this work is to use risk analysis to support the user in identifying the cause of a problems as well as deciding what actions should be developed to eliminate it. The risk analysis is then used to calculate the risk of a specific situation affecting an industrial plant. The situation, characterized by the symptoms, has a risk value which is estimated from the information stored on the system concerning the probability of occurrence of a new problem when a specific action is implemented. For the sake of system robustness it is expected that this knowledge grows along the life-cycle of an industrial plant. Depending on the knowledge available and on the risk of the situation, the adequate procedure is suggested for promptly reaction and problem elimination.

Context-Based Decision Support for Energy Efficiency and Emissions Trading in Industry: The LifeSaver Approach

This paper reports the work under development in the scope of LifeSaver project aiming at supporting manufacturing companies in optimising the energy performance of their operations. This support will be in the form of a set of ICT building blocks that combine context awareness, ambient intelligence monitoring and standard energy consumption data measurement. LifeSaver will provide: (i) information about the energy consumption to be processed in enterprise management systems, for the purpose of achieving significant energy savings; (ii) a decision support system for optimisation of energy performance of operations; and (iii) predicted cumulative data on the CO2 emissions, as input for the decision support services to enable emission trading across industries and among companies. Three demonstrators of the platform’s application in real industrial environment, and their usage for new business models, will be provided.