Previous research

[7]. SCIAD - Self-Learning Industrial Control Systems Through Process Data
Role: Team Member and Reseacher.
Host institutions: University of Coimbra (UC), and Acontrol;
Financing: co-financing by QREN, in the framework of the “Mais Centro - Regional Operational Program of the Centro”, and by the European Union through the European Regional Development Fund (ERDF); Reference number: SCIAD201121531.

Abstract: The objectives and scope of the project are in R&D on data-based control methodologies based auto-tuning and auto-adaptive approaches for PID and other controlers for linear and non-linear systems; R&D control methodologies based on process data for auto-design and auto-adaptation of controllers for linear and nonlinear systems; R&D on nonlinear control systems applying methods based on neural networks using processa data, and other methods; R&D on linear and non-linear multi-variable control methodologies; R&D of model predictive control (MPC) methodologies, considering knowledge driven and data driven approaches; R&D of MPC methods that are able to auto-tune, auto-adjust, auto-adapt, and learn the system model from observation of system variables; Research on the identification of process models by cognitive algorithms (neural networks, support vector regression (SVR), fuzzy systems, etc); Research of mathematical prediction methods andfor application on the optimisation of the MPC methods; R&D of mathematical optimisation, stability, and robustness of the researched control methodologies; R&D of learning methodologies for the determination of linguistic values of linguistic fuzzy variables, as well as for the determination of the control rules: R&D on evolutionary algorithms andor genetic algorithms andor hybrid andor optimization methodologies and/or unsupervised learning methodologies for learning the linguistic variables and the rules for the knowledge base of a fuzzy controller; R&D on application to simulated processes, real prototype processes, and real industrial processes.

[6]. Computational Intelligence Methodologies for Control of Industrial Processes
Role: PhD Student.
Place: University of Coimbra (UC), and Acontrol.
Financing: Foundation of Science and Technology (FCT); Grant reference - SFRH/BD/63383/2009.

Abstract (original summary proposal): This thesis is devoted to research on adaptive fuzzy controllers, predictive control, and intelligent control methodologies such as neural and neuro-fuzzy control for industrial nonlinear andor time-varying plants. Nonlinear andor time varying processes are difficult to control due to their complexity. The issues of varying parameters, presence of disturbances, non-modeled dynamics, robustness, and stability will be addressed. The developed methodologies will be validated on a main case study concerning the control of NOx and SOx emissions on a cement kiln. The process is nonlinear time-varying exhibiting the above mentioned problems. Presently, there are no automatic methodologies to control these emissions under the legal limits. We intend to research control methodologies, integrating human knowledge and/or adaptivity towards improved solutions, for the kiln and treatment systems, having large impact on the amount emission removal chemicals. These chemicals have large economic costs comparable to the maintenance costs of all the kiln electrical systems.

[5]. FAir-Control - Factory Air Pollution Control
Role: Team Member.
Host institutions: University of Coimbra (UC), and Acontrol;
Financing: Eurostars Programme of the EUREKA network, financed by “Fundação para a Ciência e a Tecnologia” (FCT), of the Ministry of Education and Science, “Agência de Inovação” (AdI), and the Seventh Framework Programme for Research and Technological Development (FP7) of the European Union; Reference number: E!6498.

Abstract: The aim of this project is to develop advanced real-time control methodologies for cost optimization of air pollutants mitigation systems. The goal is the automatic control and optimization of the feed rates of mitigation chemicals introduced into the production process (Selective Non-Catalytic Reduction), in order to control the pollutants output on the stack within the legal limits, i.e. always taking in consideration the non-violation of regulations. The main objective is to control the pollutants output on the stack within the legal limits by optimally and automatically selecting the chemicals injection rate into the cement production process, always taking in consideration the non-violation of regulations. Controlling the pollutants ration is a quite difficult problem due to the large instability of the chemicals reactions. Hence the operators work to comply to a worst case scenario, protecting themselves by injecting a quantity of chemicals much higher than needed, wasting resources and increasing the production costs. This project proposes the development of a software-based product, with embedded computational intelligence, capable of selecting the optimal chemical injection quantities for minimizing the operational cost. The base computational intelligence technology for this project is the Fuzzy Logic control. The chemical reactions generated by the chemicals introduced in the cement kiln are very complex and its effectiveness highly depends on several factors such as: temperature, pollutants concentration and dispersion of the chemicals into the gas. One other aim of this project is to develop a systems that is capable to change its strategy of injecting chemicals according to the variations in the process. To successfully achieve these objectives, it will be formed a consortium composed by 3 organizations: (1) Industrial control expert company (Acontrol), (2) Burner and kiln process expert company (Pricast), (3) University research group with expertise in advanced control and cognitive systems (UC). The impact of the project results on the process optimization, with direct reduction of factory costs, will add a high market potential to the consortium participants.

[4]. SInCACI - Intelligent Systems for Industrial Control, Acquisition and Communication
Role: Reseacher.
Host institutions: University of Coimbra (UC), and Acontrol;
Financing: “Mais Centro Operacional Program”, financed by “European Regional Development Fund” (ERDF), and “Agência de Inovação” (AdI); Reference number: SInCACI31202009.

Abstract: The objectives and scope of the project are performing R&D on intelligent control and decision methods; Development of a general-purpose Fuzzy Control System (FCS); On-line process control and monitoring; Controller specification and implementation; Visualization of the process state; Devicesprocess failure reports; Direct application to industry problems; Research computational intelligence techniques for the development of soft sensors for industrial application; R&D on industrial communication and processing modules: industrial distributed real-time communication fieldbus protocols: ControlNet, EthernetIP, DeviceNet; ProfiNet. Relevant properties to industrial fieldbuses: Real-time operation, Reliability, Deterministic, Error-proof, Easy to extend and maintain. Direct application to industry problems; Selling on market.

[3]. Project of Creation of the Research & Development Division of Acontrol
Role: Reseacher.
Host institution: University of Coimbra (UC); Principal contractor: Acontrol;
Financing: “IAPMEI - Instituto de Apoio às Pequenas e Médias Empresas e à Inovação” and “AControl - Automação e Controle, Lda”. Financed within ‘‘Projectos de Criação e Reforço de Competências Internas de I&DT’’ of ‘‘Sistema de Incentivos à Investigação e Desenvolvimento Tecnológico nas Empresas’’ (SI I&DT) of ‘‘Quadro de Referência Estratégico Nacional Portugal 2007-2013’’ (QREN); Reference number: CENTRO-07-0202-FEDER-002502.

Abstract: The University of Coimbra participates as a specialized consultant in the area of intelligent systems and algorithms.

[2]. FUZCTR - Development of Fuzzy Controllers for Modules of Manufacturing Systems
Role: Reseacher.
Host institution: “Institute of Systems and Robotics - University of Coimbra” (ISR-UC);
Financing: “AControl - Automação e Controle Industrial, Lda” company; Reference number: ACONTROLISR001/2007.

Abstract: In this project the goal is to study and implement a fuzzy control system for industrial process control applications. The first applications will be in a cement kiln plant, including, as a first step, the control of the raw mill process.

[1]. STRNET - Development of Hardware/Software for Industrial Distributed Real-Time Systems Using the ControlNet Protocol
Role: Reseacher.
Host institution: “Institute of Systems and Robotics - University of Coimbra” (ISR-UC);
Financing: “AControl - Automação e Controle Industrial, Lda” company; Reference number: ACONTROLISR002/2007.

Abstract: In this project the goal is to study and develop hardware and software of real-time system containint digital processing (microcontrollers) having the capacity of performing network communication with other modules using the ControlNet protocol, as well as the capacity of performing relevant processings in industrial automation and control environments.