Objective
The objective of this project proposal is to develop a Predictive Emission Moni toring System (PEMS) for on-line and in real-time monitoring and optimisation o f pollutant emissions (NOx S02, C02, CO and particles) and combustion efficienc y of industrial process. The application of PEMS will provide a reduction of th e environmental measures cost of those pollutants to 50%. This system will also be able to achieve and provide references to increase the process efficiency a bout 0.5% in boiler of industrial process and environmental control systems. In addition, this system will be applicable to other kind of industries included under 96/61/CE Directive. The Predictive System has been defined as an alternat ive to Continuous Emission Monitoring System, based on its advantages of low co st (50%), lower maintenance (50-60%). higher reliability and better emission co ntrol. Moreover, all European and international companies are involved in strat egies and policies to reduce costs into every sectors; the aim of this project is to make a clear contribution in this context. taking into consideration that the measurement cost will be reduce to 50%. On the other hand and considering the current environmental strategic goal in European and in the World, an speci ally the C02 reduction with low cost, the new Predictive System proposed is a c lear contribution to achieve this goal in industrial process which have a high contribution to generate air emission pollutants. Therefore, the development of the system is framed within a fundamental field for E.U. due to the fact such a system contributes to increase the European competitiveness against other cou ntries such as the United States, Japan and Canada. The main objective will be achieved applying a five phases methodology: 1. Data compilation regarding ope rating condition and pollutant emissions of case-study plant. 2. Application o f Computational Fluid Dynamics (C.F.D.) stack/duct model to perform and develop emission characterisation and define the optimum sensor location to carry out the measurement campaign. 3. Development of predictor module, based on neural network applications which will teach from case-study plant, manual measurement campaign and C.F.D. boiler model used for the generation of supplementary data . 4. After developing the predictor module, the optimisation system will be de veloped to analyse and make up decisions based on the compilation of plant oper ation expertise to establish the decision rules and the simulation of different operating scenarios. 5. Finally, the implementation and validation of a soft ware support tool will be carry out. The consortium is made up of organisation s with a wide experience of chemical, mechanical and environmental engineering issues: three engineering firms, a Spanish company specialising in modelling an d emission characterisation using Computational Fluid Dynamics, a Portuguese RO R specialist in the development of measuring and sampling techniques and method s, and a British firm which expertise is in the area of mathematical modelling on neural networks, in addition to a French software firm responsible for the d evelopment of the final software and a Spanish electric utility as end-user. Fi nally the project is included within the scope of area I of BRITE-EURAM Program me: " Production Technologies": Area 1.2 "Development of clean production techn ologies"; covering objectives: 1.2.4.S. "Upgrading and adaptation of existing t echnology in order to comply with the new and future regulations concerning the environment and health protection", 1.2.1 .M "Development of tools and methodo logies to analyse material flows for the identification of pollution prevention and safety enhancement opportunities and assessment of the evolution of pollut ion risks", 1.2.3.M "Development of reliable multi-detection sensors and their integration with actuators and vessels for the real-time measurement and advanc ed control methodologies and their integration into chemical and physical proce ss" and 1.2.5.M "Process optimisation and identification of more efficient proc ess configurations, reducing waste, and enabling the safe reconfiguration of pr ocesses as an ongoing activity, using in particular simulation and modelling".
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systems
- engineering and technologyenvironmental engineering
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencesphysical sciencesclassical mechanicsfluid mechanicsfluid dynamicscomputational fluid dynamics
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Call for proposal
Data not availableFunding Scheme
CSC - Cost-sharing contractsCoordinator
28709 SAN SEBASTIAN DE LOS REYES - MADRID
Spain