Modelling of Temperature and Syngas Composition in a Fixed Bed Biomass Gasifier using Nonlinear Autoregressive Networks

To improve biomass gasification efficiency through process control, a lot of attention had been given to development of models that can predict process parameters in real time and changing operating conditions. The paper analyses the potential of a nonlinear autoregressive exogenous model to predict syngas temperature and composition during plant operation with variable operating conditions. The model has been designed and trained based on measurement data containing fuel and air flow rates, from a 75 kWth fixed bed gasification plant at Technical University Dresden. Process performance changes were observed between two sets of measurements conducted in 2006 and 2013. The effect of process performance changes on the syngas temperature was predicted with prediction error under 10% without changing the model structure. It was concluded that the model could be used for short term predictions (up to 5 minutes) of syngas temperature and composition as it strongly depends on current process measurements for future predictions. For long term predictions other types of dynamic neural networks are more applicable

Improvement of existing coal fired thermal power plants performance by control systems modifications

This paper presents possibilities of implementation of advanced combustion control concepts in selected Western Balkan thermal power plant, and particularly those based on artificial intelligence as part of primary measures for nitrogen oxide reduction in order to optimise combustion and to increase plant efficiency. Both considered goals comply with environmental quality standards prescribed in large combustion plant directive. Due to specific characterisation of Western Balkan power sector these goals should be reached by low cost and easily implementable solution. Advanced self-learning controller has been developed and the effects of advanced control concept on combustion process have been analysed using artificial neural-network based parameter prediction model. (c) 2013 Elsevier Ltd. All rights reserved

Improvement of environmental aspects of thermal power plant operation by advanced control concepts

The necessity of the reduction of greenhouse gas emissions, as formulated in the Kyoto Protocol, imposes the need for improving environmental aspects of existing thermal power plants operation. Improvements can be reached either by efficiency increment or by implementation of emission reduction measures. Investments in refurbishment of existing plant components or in plant upgrading by flue gas desulphurization, by primary and secondary measures of nitrogen oxides reduction, or by biomass co-firing, are usually accompanied by modernisation of thermal power plant instrumentation and control system including sensors, equipment diagnostics and advanced controls. Impact of advanced control solutions implementation depends on technical characteristics and status of existing instrumentation and control systems as well as on design characteristics and actual conditions of installed plant components. Evaluation of adequacy of implementation of advanced control concepts is especially important in Western Balkan region where thermal power plants portfolio is rather diversified in terms of size, type and commissioning year and where generally poor maintenance and lack of investments in power generation sector resulted in high greenhouse gases emissions and low efficiency of plants in operation. This paper is intended to present possibilities of implementation of advanced control concepts, and particularly those based on artificial intelligence, in selected thermal power plants in order to increase plant efficiency and to lower pollutants emissions and to comply with environmental quality standards prescribed in large combustion plant directive. [Acknowledgements. This paper has been created within WBalkICT - Supporting Common RTD actions in WBCs for developing Low Cost and Low Risk ICT based solutions for TPPs Energy Efficiency increasing, SEE-ERA.NET plus project in cooperation among partners from IPA SA - Romania, University of Zagreb - Croatia and Vinca Institute from Serbia and. The project has initiated a strong scientific cooperation, with innovative approaches, high scientific level, in order to correlate in an optimal form, using ICT last generation solutions, the procedures and techniques from fossil fuels burning processes thermodynamics, mathematical modelling, modern methods of flue gases analysis, combustion control, Artificial Intelligence Systems with focus on Expert Systems category.

Performance Analysis of a Hybrid District Heating System: A Case Study of a Small Town in Croatia

Hybridisation of district heating systems can contribute to more efficient heat generation through cogeneration power plants or through an increase in the share of renewable energy sources in total energy consumption while reducing negative aspects of particular energy source utilisation. In this work, the performance of a hybrid district energy system for a small town in Croatia has been analysed. A mathematical model for process analysis and optimisation algorithm for optimal system configuration have been developed and described. The main goal of the system optimisation is to reduce heat production costs. Several energy sources for heat production have been considered in 8 different simulation cases. Simulation results show that the heat production costs could be reduced with introduction of different energy systems into an existing district heating system. Renewable energy based district heating systems could contribute to heat production costs decrease in district heating systems up to 30% in comparison with highly efficient heat production technologies based on conventional fuels

Matematičko modeliranje procesa rasplinjavanja biomase u reaktorima s nepomičnim slojem

The aim of the research was to develop a mathematical model that is capable to predict process parameters with reasonable speed and accuracy in different and changeable operating conditions during biomass gasification. Process parameters such as fuel and air flow rate were considered as one of the model inputs which lead to prediction of other process parameters such as syngas temperature and syngas composition. Process dynamics were modelled and simulaiton results were analysed in order to enable further development of an on-line gasification process control concept. Model was designed to predict process parameters in different and changing operating conditions. For model development purposes different equlibrium models and artificial inteligence based models were utilised and their performance was analysed. Model prediction potential was validated on measurement data from a fixed-bed type gasification facility. Developed models were able to predict process parameters such as syngas temperature with average prediction error below 10% (R2 > 0.82) and syngas composition with average prediction error below 38% (R2 > 0.42).Dynamic modelling approach with active prediction error estimation was developed to predict process parameters in variable operating conditions. Models were further used to develop a control strategy that could improve process efficiency by 25%.Cilj istraživanja bio je razvoj matematičkog modela koji će predvidjeti pogonske parametre s razumnom brzinom i točnošću u različitim i promjenjivim pogonskim uvjetima tokom rasplinjavanja. Pogonski parametri poput protoka goriva i zraka uzeti su u obzir kao jedni od ulaznih parametara koji su korišteni za predviđanje pogonskih parametara kao što su temperatura i sastav sintetskog plina. Dinamika procesa je modelirana i rezultati simulacije su analizirani u svrhu budućeg razvoja regulacijskih sustava procesa rasplinjavanja. Model je u stanju predviditi pogonske parametre u različitim i promijenjivim pogonskim uvjetima. Za razvoj modela korišteni su različiti modeli temeljeni na očuvanju mase i energije kao i modeli temeljeni na umjetnoj inteligenciji te će se analizirati njihove značajke. Predikcijski potencijal modelavalidiran je na temelju mjerenih podataka prikupljenih s rasplinjača s nepomičnim slojem. Razvijeni modeli su u mogućnosti predvidjeti procesne parametre kao što je temperatura sintetskog plina s prosječnom greškom prdvuđanja ispod 10% (R2 > 0.82) te sastav sintetskog plina s greškom predviđanja ispod 38% (R2 > 0.42). Dinamični pristup modeliranju koji uključuje aktivnu analizu greške predviđanja korišten je za predviđanje pogonskih parametara u promjenjivim uvjetima. Razvijeni modeli su dalje korišteni za razvoj nove strategije vođenja postrojenja koja ima potencijal poboljšati efikasnost procesa za 25%

Matematički model i simulacija regulacijskog kruga temperature pregrijane pare

Ovaj rad se bavi problematikom modeliranja termohidrauličnih procesa u pregrijačkom dijelu termoenergetskog postrojenja. Razmotren je utjecaj temperature pare na iskoristivost i životni vijek parno-turbinskih procesa, te prednosti i nedostaci pregrijanja pare. Obrazložena je potreba i značaj regulacije temperature pare u parno-turbinskim postrojenjima. Razvijeni su matematički modeli pregrijača pare. Odabrani su i opisani koncepti regulacije temperature pare iz tehničke primjene i predložen optimalni koncept regulacije temperature pare. Usporedba djelovanja odabranih koncepata regulacije je izvršena pomoću rezultata simulacije dinamičkih pojava zatvorenog kruga unutar pregrijača pare u različitim pogonskim stanjima

Hybrid solar thermal power plant operating strategy

Tehnologija proizvodnje električne energije u postrojenjima s Rankineovim ciklusom koja kao primarni izvor energije koriste sunčevu energiju tzv. solarnim termoelektranama intenzivnije se razvija u mediteranskim zemljama i SAD-u od 2006. godine zahvaljujući poticajnim tarifama. Trenutno su najraširenija postrojenja u kojima se izravno sunčevo zračenje usmjerava pomoću paraboličnih zrcala na cijev u kojoj struji i zagrijava se termičko ulje. Vruće termičko ulje koristi se za proizvodnju vodene pare, radnog medija u Rankineovom ciklusu. Suvremene solarne termoelektrane uobičajeno se izvode s toplinskim spremnikom te s dodatnim zagrijačem jednog od radnih medija koji omogućavaju produljenje pogona na nazivnoj snazi i u uvjetima smanjenog sunčevog zračenja. Rad se bavi problematikom modeliranja termohidrauličnih procesa u solarnoj termoelektrani. Opisani su glavni dijelovi konvencionalne solarne termoelektrane. Razvijen je matematički model dinamike termohidrauličkih procesa u solarnoj termoelektrani. Obrazložena je potreba i značaj, te su opisani koncepti regulacije temperature termo ulja u kolektorskom sustavu. Uspoređeni su različiti koncepti vođenja postrojenja solarne termoelektrane pomoću rezultata simulacije matematičkog modela. Pokazano je da hibridni koncept sa dodatnim zagrijačem i spremnikom topline omogućava približno kontinuirani pogon postrojenja u većem dijelu dana. Provedena je detaljna numerička analiza naprezanja u cijevnoj stijeni pregrijača pare.Due to establishment of incentive framework, technology of energy production in solar Rankine cycle power plants has been more intensively developed in USA and Mediterranean countries since 2006. In most common configuration of solar thermal power plants parabolic mirrors are used for concentration of solar irradiation onto absorber tube and for heating of thermal oil. Hot thermal oil is further used to produce superheated steam, working fluid in conventional Rankine cycle. State of the art solar thermal power plants are equipped with heat storage system and additional burner of fossil/or renewable fuels enabling increase of power plant production even in conditions of lower solar irradiation. This paper deals with mathematical modeling of thermo hydraulic processes in solar power plant. Main components of conventional solar thermal power plant have been described and theirs mathematical models have been developed. Various concepts of thermal oil temperature control have been analyzed. Simulation of solar power plant operation in typical meteorological conditions has been performed and different operation strategies have been compared. It has been shown that configuration with ancillary burner and thermal storage enables approximately continuous operation of solar plant. Detail numerical analysis of stresses occurring in superheater tube sheet has been performed

Matematičko modeliranje procesa rasplinjavanja biomase u reaktorima s nepomičnim slojem

The aim of the research was to develop a mathematical model that is capable to predict process parameters with reasonable speed and accuracy in different and changeable operating conditions during biomass gasification. Process parameters such as fuel and air flow rate were considered as one of the model inputs which lead to prediction of other process parameters such as syngas temperature and syngas composition. Process dynamics were modelled and simulaiton results were analysed in order to enable further development of an on-line gasification process control concept. Model was designed to predict process parameters in different and changing operating conditions. For model development purposes different equlibrium models and artificial inteligence based models were utilised and their performance was analysed. Model prediction potential was validated on measurement data from a fixed-bed type gasification facility. Developed models were able to predict process parameters such as syngas temperature with average prediction error below 10% (R2 > 0.82) and syngas composition with average prediction error below 38% (R2 > 0.42).Dynamic modelling approach with active prediction error estimation was developed to predict process parameters in variable operating conditions. Models were further used to develop a control strategy that could improve process efficiency by 25%.Cilj istraživanja bio je razvoj matematičkog modela koji će predvidjeti pogonske parametre s razumnom brzinom i točnošću u različitim i promjenjivim pogonskim uvjetima tokom rasplinjavanja. Pogonski parametri poput protoka goriva i zraka uzeti su u obzir kao jedni od ulaznih parametara koji su korišteni za predviđanje pogonskih parametara kao što su temperatura i sastav sintetskog plina. Dinamika procesa je modelirana i rezultati simulacije su analizirani u svrhu budućeg razvoja regulacijskih sustava procesa rasplinjavanja. Model je u stanju predviditi pogonske parametre u različitim i promijenjivim pogonskim uvjetima. Za razvoj modela korišteni su različiti modeli temeljeni na očuvanju mase i energije kao i modeli temeljeni na umjetnoj inteligenciji te će se analizirati njihove značajke. Predikcijski potencijal modelavalidiran je na temelju mjerenih podataka prikupljenih s rasplinjača s nepomičnim slojem. Razvijeni modeli su u mogućnosti predvidjeti procesne parametre kao što je temperatura sintetskog plina s prosječnom greškom prdvuđanja ispod 10% (R2 > 0.82) te sastav sintetskog plina s greškom predviđanja ispod 38% (R2 > 0.42). Dinamični pristup modeliranju koji uključuje aktivnu analizu greške predviđanja korišten je za predviđanje pogonskih parametara u promjenjivim uvjetima. Razvijeni modeli su dalje korišteni za razvoj nove strategije vođenja postrojenja koja ima potencijal poboljšati efikasnost procesa za 25%