The work deals with the verification of functionality of moisture content sensors MCT460-WP from the company Process sensors to measure fuel and products from biomass. The sensor is located on one of the Vyncke company boilers. In the first part of thesis are introduced basic issues of biomass. Thesis also generally describes the control and dynamics behaviour of biomass boilers and the effect of input parameters on the output parameters. The last part deals with comparing and analysing measured values with the calculated values and effects of moisture content changes on the output parameters of the boiler. As the last issue is loaded conceptual design based on on-line moisture sensing and inclusion of auxiliary fuel control into the existing system in order to stabilize the processes in the combustion chamber and output parameters. This should result in lower financial demands during boiler operation.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1979. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Includes bibliographical references. / by Paul David Bayless. / M.S.

O desafio para a área de monitoramento de processos, em indústrias químicas, ainda é a etapa de detecção, com a necessidade de desenvolvimento de sistemas confiáveis. Pode-se resumir que um sistema é confiável, ao ser capaz de detectar as situações anormais, de modo precoce, e, ao mesmo tempo, de minimizar a geração de alarmes falsos. Ao se ter um sistema confiável, pode-se empregá-lo para auxiliar o operador, de fábricas, no processo de tomada de decisões. O objetivo deste estudo é apresentar uma metodologia, baseada na técnica, modelo oculto de Markov (HMM, acrônimo de ?Hidden Markov Model?), para se detectar situações anormais em caldeiras de recuperação química. As aplicações de maior sucesso de HMM são na área de reconhecimento de fala. Pode-se citar como aspectos positivos: o raciocínio probabilístico, a modelagem explícita, e a identificação a partir de dados históricos. Fez-se duas aplicações. O primeiro estudo de caso é no ?benchmark? de um sistema de evaporação múltiplo efeito de uma fábrica de produção de açúcar. Identificou-se um HMM, característico de operação normal, para se detectar cinco situações anormais no atuador responsável por regular o fluxo de xarope de açúcar para o primeiro evaporador. A detecção, para as três situações abruptas, é imediata, uma vez que o HMM foi capaz de detectar alterações, abruptas, no sinal da variável monitorada. Em relação às duas situações incipientes, foi possível detectá-las ainda em estágio inicial; ao ser o valor de f (vetor responsável por representar a intensidade de um evento anormal, com o tempo), no instante da detecção, próximo a zero, igual a 2,8% e 2,1%, respectivamente. O segundo estudo de caso é em uma caldeira de recuperação química, de uma fábrica de produção de celulose, no Brasil. O objetivo é monitorar o acúmulo de depósitos de cinzas sobre os equipamentos da sessão de transferência de calor convectivo, através de medições de perda de carga. Este é um dos principais desafios para se aumentar a eficiência operacional deste equipamento. Após a identificação de um HMM característico de perda de carga alta, pôde-se verificar a sua capacidade de informar o estado atual e, por consequência, a tendência do sistema, de modo similar à um preditor. Pôde-se demonstrar também a utilidade de se definir limites de controle, com o objetivo de se ter a informação sobre a distância entre o estado atual e os níveis de alarme de perda de carga. / The greatest challenge faced by the area of process monitoring in chemical industries still resides in the fault detection task, which aims at developing reliable systems. One may say that a system is reliable if it is able to perform early fault detection and, at the same time, to reduce the generation of false alarms. Once there is a reliable system available, it can be employed to help operators, in factories, in the decisionmaking process. The aim of this study is presenting a methodology, based on the Hidden Markov Model (HMM) technique, suggesting its use in the detection of abnormal situations in chemical recovery boilers. The most successful applications of HMM are in the area of speech recognition. Some of its advantages are: probabilistic reasoning, explicit modeling and the identification based on process history data. This study discusses two applications. The first one is on a benchmark of a multiple evaporation system in a sugar factory. A HMM representative of the normal operation was identified, in order to detect five abnormal situations at the actuator responsible for controlling the syrup flow to the first evaporator. The detection result for the three abrupt situations was immediate, since the HMM was capable of detecting the statistical changes on the signal of the monitored variable as soon as they occurred. Regarding to the two incipient situations, the detection was done at an early stage. For both events, the value of vector f (responsible for representing the strength of an abnormal event over time), at the time it occurred, was near zero, equal to 2.8 and 2.1%, respectively. The second case study deals with the application of HMM in a chemical recovery boiler, belonging to a cellulose mill, in Brazil. The aim is monitoring the accumulation of ash deposits over the equipments of the convective heat transfer section, through pressure drop measures. This is one of the main challenges to be overcome nowadays, bearing in mind the interest that exists in increasing the operational efficiency of this equipment. Initially, a HMM for high values of pressure drop was identified. With this model, it was possible to check its capacity to inform the current state, and consequently, the tendency of the system (similarly as a predictor). It was also possible to show the utility of defining control limits, in order to inform the operator the relative distance between the current state of the system and the alarm levels of pressure drop.

O desafio para a área de monitoramento de processos, em indústrias químicas, ainda é a etapa de detecção, com a necessidade de desenvolvimento de sistemas confiáveis. Pode-se resumir que um sistema é confiável, ao ser capaz de detectar as situações anormais, de modo precoce, e, ao mesmo tempo, de minimizar a geração de alarmes falsos. Ao se ter um sistema confiável, pode-se empregá-lo para auxiliar o operador, de fábricas, no processo de tomada de decisões. O objetivo deste estudo é apresentar uma metodologia, baseada na técnica, modelo oculto de Markov (HMM, acrônimo de ?Hidden Markov Model?), para se detectar situações anormais em caldeiras de recuperação química. As aplicações de maior sucesso de HMM são na área de reconhecimento de fala. Pode-se citar como aspectos positivos: o raciocínio probabilístico, a modelagem explícita, e a identificação a partir de dados históricos. Fez-se duas aplicações. O primeiro estudo de caso é no ?benchmark? de um sistema de evaporação múltiplo efeito de uma fábrica de produção de açúcar. Identificou-se um HMM, característico de operação normal, para se detectar cinco situações anormais no atuador responsável por regular o fluxo de xarope de açúcar para o primeiro evaporador. A detecção, para as três situações abruptas, é imediata, uma vez que o HMM foi capaz de detectar alterações, abruptas, no sinal da variável monitorada. Em relação às duas situações incipientes, foi possível detectá-las ainda em estágio inicial; ao ser o valor de f (vetor responsável por representar a intensidade de um evento anormal, com o tempo), no instante da detecção, próximo a zero, igual a 2,8% e 2,1%, respectivamente. O segundo estudo de caso é em uma caldeira de recuperação química, de uma fábrica de produção de celulose, no Brasil. O objetivo é monitorar o acúmulo de depósitos de cinzas sobre os equipamentos da sessão de transferência de calor convectivo, através de medições de perda de carga. Este é um dos principais desafios para se aumentar a eficiência operacional deste equipamento. Após a identificação de um HMM característico de perda de carga alta, pôde-se verificar a sua capacidade de informar o estado atual e, por consequência, a tendência do sistema, de modo similar à um preditor. Pôde-se demonstrar também a utilidade de se definir limites de controle, com o objetivo de se ter a informação sobre a distância entre o estado atual e os níveis de alarme de perda de carga. / The greatest challenge faced by the area of process monitoring in chemical industries still resides in the fault detection task, which aims at developing reliable systems. One may say that a system is reliable if it is able to perform early fault detection and, at the same time, to reduce the generation of false alarms. Once there is a reliable system available, it can be employed to help operators, in factories, in the decisionmaking process. The aim of this study is presenting a methodology, based on the Hidden Markov Model (HMM) technique, suggesting its use in the detection of abnormal situations in chemical recovery boilers. The most successful applications of HMM are in the area of speech recognition. Some of its advantages are: probabilistic reasoning, explicit modeling and the identification based on process history data. This study discusses two applications. The first one is on a benchmark of a multiple evaporation system in a sugar factory. A HMM representative of the normal operation was identified, in order to detect five abnormal situations at the actuator responsible for controlling the syrup flow to the first evaporator. The detection result for the three abrupt situations was immediate, since the HMM was capable of detecting the statistical changes on the signal of the monitored variable as soon as they occurred. Regarding to the two incipient situations, the detection was done at an early stage. For both events, the value of vector f (responsible for representing the strength of an abnormal event over time), at the time it occurred, was near zero, equal to 2.8 and 2.1%, respectively. The second case study deals with the application of HMM in a chemical recovery boiler, belonging to a cellulose mill, in Brazil. The aim is monitoring the accumulation of ash deposits over the equipments of the convective heat transfer section, through pressure drop measures. This is one of the main challenges to be overcome nowadays, bearing in mind the interest that exists in increasing the operational efficiency of this equipment. Initially, a HMM for high values of pressure drop was identified. With this model, it was possible to check its capacity to inform the current state, and consequently, the tendency of the system (similarly as a predictor). It was also possible to show the utility of defining control limits, in order to inform the operator the relative distance between the current state of the system and the alarm levels of pressure drop.

The use of renewable wood-based fuel has been increasing in the last few decades because it is said to be carbon neutral. However, wood-based fuel, and especially used wood (also known as recycled wood or waste wood), is more corrosive than virgin wood (forest fuel), because of higher amounts of chlorine and heavy metals. These elements increase the corrosion problems at the furnace walls where the oxygen level is low. Corrosion mechanisms are usually investigated at the superheaters where the temperature of the material and the oxygen level is higher than at the furnace walls.  Much less work has been performed on furnace wall corrosion in wood or used wood fired boilers, which is the reason for this project.    Tests are also mostly performed under simplified conditions in laboratories, making the results easier to interpret.  In power plants the interpretation is more complicated. Difficulties in the study of corrosion processes are caused by several factors such as deposit composition, flue gas composition, boiler design, and combustion characteristics and so on. Therefore, the laboratory tests should be a complement to the field test ones. This doctoral project involved in-situ testing at the furnace wall of power boilers and may thus contribute to fill the gap. The base material for furnace walls is a low alloy steel, usually 16Mo3, and the tubes may be coated or uncoated. Therefore tests were performed both on 16Mo3 and more highly alloyed materials suitable for protective coatings. Different types of samples exposed in used-wood fired boilers were analysed by different techniques such as LOM (light optical microscopy), XRD (X-ray diffraction), SEM (scanning electron microscopy), EDS (energy dispersive spectroscopy), WDS (wavelength dispersive spectroscopy), FIB (focused ion beam) and GD-OES (glow discharge optical emission spectroscopy). The corrosion rate was measured. The environment was also thermodynamically modelled by TC (Thermo-Calc ®). The results showed that 16Mo3 in the furnace wall region is attacked by HCl, leading to the formation of iron chloride and a simultaneous oxidation of the iron chloride. The iron chloride layer appeared to reach a steady state thickness.   Long term exposures showed that A 625 (nickel chromium alloy) and Kanthal APMT (iron-chromium-aluminium alloy) had the lowest corrosion rate (about 25-30% of the rate for 16Mo3), closely followed by 310S (stainless steel), making these alloys suitable for coating materials. It was found that the different alloys were attacked by different species, although they were exposed in the boiler at the same time in the same place. The dominant corrosion process in the A 625 samples seemed to be by a potassium-lead combination, while lead did not attack the APMT samples. Potassium attacked the alumina layer in the APMT samples, leading to the formation of a low-protective aluminate and chlorine was found to attack the base material.  The results showed that stainless steels are attacked by both mechanisms (Cl- induced attack and K-Pb combination). Decreasing the temperature of the furnace walls of a waste wood fired boiler could decrease the corrosion rate of 16Mo3. However, this low corrosion rate corresponds to a low final steam pressure of the power plant, which in not beneficial for the electrical efficiency. The short term testing results showed that co-firing of sewage sludge with used wood can lead to a reduction in the deposition of K and Cl on the furnace wall during short term testing. This led to corrosion reduction of furnace wall materials and coatings. The alkali chlorides could react with the aluminosilicates in the sludge and be converted to alkali silicates. The chromia layer in A 625 and alumina in APMT were maintained with the addition of sludge. / Förnybara träbaserade bränslen har ökat i användning under de senaste decennierna, eftersom det är koldioxidneutrala. Emellertid är träbaserade bränslen, och i synnerhet använt trä (även känt som återvunnet trä, returträ eller träavfall), mer korrosivt än skogsbränsle, på grund av högre halter klor och tungmetaller. Dessa ökar korrosionsproblemen på eldstadsväggarna, särskilt på platser där syrehalten är låg. Korrosionsmekanismer undersöks vanligtvis på överhettare dvs. på områden där materialets temperatur och syrenivån är högre än vid eldstadsväggarna. Färre arbeten har utförts på eldstadskorrosion i returträ pannor, vilket är motiveringen till detta projekt. Normalt sätt så görs endast i laboratorietester där resultaten är lättare att tolka. I kraftverk är tolkningen mer komplicerad. Undersökningar av korrosionsprocesser försvåras av flera faktorer såsom panndesign, förbränningsegenskaper, rökgassammansättning, beläggningskemi och så vidare. Därför bör laboratorietester kompletteras med fältförsök. Detta doktorandprojekt kan således bidra till att fylla denna brist. Eldstadsväggarna är uppbyggda av flera rör som svetsas samman och de består vanligtvis av 16Mo3 stål. Rören kan vara belagda eller obelagda. Tester har därför genomförts på 16Mo3 samt på höglegerade material vilka är lämpliga som skyddande beläggningar. Olika typer av prov som exponerats i förbränningspannor av returträ analyserades med olika tekniker såsom SEM (svepelektronmikroskopi), EDS (energidispersiv spektroskopi), WDS (våglängd dispersiv spektroskopi), FIB (fokuserad jonstråle) LOM (ljusoptisk mikroskopi), XRD (röntgendiffraktion), och GD-OES (glimurladdning med optisk emissionsspektroskopi). Miljön samt korrosionsprocesser har modellerats termodynamiskt med mjukvaran TC (Termo-Calc®). Resultaten visade att 16Mo3 i eldstadsväggen angrips av väteklorid, vilket leder till bildning av järnklorid och en samtidig oxidation av järnkloriden. Järnkloridskiktet verkade nå ett stationärt tillstånd vad avser tjocklek. Sex veckors prov visade att A 625 (nickelkromlegering) och Kanthal APMT (järnkromaluminiumlegering) hade den lägsta korrosionshastigheten (ca 25-30% av korrosionshastigheten för 16Mo3), följt av 310S (rostfritt stål). Vi har funnit att de olika legeringarna angrips genom olika mekanismer, även om de var exponerade i pannan samtidigt på samma plats. Den dominerande korrosionsmekanismen för legeringen A 625 verkar i huvudsak bero på kalium och bly, medan bly inte attackerar Kanthal APMT. Kalium angriper aluminiumoxidskiktet på Kanthal APMT, vilket leder till bildning av icke-skyddande aluminat medan klor i sin tur attackerar basmaterialet. Resultaten visar att rostfritt stål attackeras genom klor-inducerad korrosion samt kalium och bly i kombination. Reducering av temperaturen kan minska korrosionshastigheten hos 16Mo3. Men denna lägre korrosionshastighet motsvarar ett lågt slutligt ångtryck hos kraftverket, vilket inte är fördelaktigt för elverkningsgraden. De kortare exponeringarna visade att samtidig förbränning av avloppsslam med returträ kan leda till minskad avsättning av kalium och klor i form av alkaliklorider på eldstadsväggarna. Detta ledde till korrosionsminskning av alla studerade material. Dessa alkaliklorider skulle kunna reagera med aluminiumsilikaterna från slammet och omvandlas till alkalisilikater. Detta verkar minska den alkali-inducerade korrosionen på A 625, APMT och 310S. Den aluminiumoxid som bildades på APMT och det kromoxidskikt som bildades på A 625 upprätthölls med tillsats av slam. / <p>QC 20151015</p>

The subject of this diploma thesis is the design of heating and hot water in the administrative building in Zlin on Jižní Svahy. The building has five floors above ground and one underground floor. There are designed two variants of heat source for heating and hot water (transfer sta-tions and gas boiler). Part A solves water quality in heating systems. Part B deals with design of the heating system, hot water heating, insurance and facility expansion, technical report and drawings heating in the building. The last part C is the experimental measurement of the quality of heating water in the CZT systems.

This work deals with the determination of the operational efficiency of solid fules boilers for their inclusion in the relevant emission class according to ČSN EN 303-5. Two steel boilers of the same manufacturer, type and performance but of different ages, were measured. The experimental measurments in the real operation of the boilers were carrien out during the combustion of two types of fuels, namely brown coal and spruce wood.The operational efficiency of the monitored sources was calculated from the measured data using both direct and undirect methods. The found efficiency of the boilers corresponds to the expected values, due to their construction, age and the way of using individual components and regulations. In the project part of this work there is a proposal of heating of the family house for the condition of the building before and after the insulation. The calculation was made for the solid fuel boiler and heat pump. The building is heated by radiators and floor heating. The design of the whole system, technical report and project documentation were prepared for each variant.

In the hope to increase the detection rate of faults in combined heat and power plant boilers thus lowering unplanned maintenance three machine learning models are constructed and evaluated. The algorithms; k-Nearest Neighbor, One-Class Support Vector Machine, and Auto-encoder have a proven track record in research for anomaly detection, but are relatively unexplored for industrial applications such as this one due to the difficulty in collecting non-artificial labeled data in the field.The baseline versions of the k-Nearest Neighbor and Auto-encoder performed very similarly. Nevertheless, the Auto-encoder was slightly better and reached an area under the precision-recall curve (AUPRC) of 0.966 and 0.615 on the trainingand test period, respectively. However, no sufficiently good results were reached with the One-Class Support Vector Machine. The Auto-encoder was made more sophisticated to see how much performance could be increased. It was found that the AUPRC could be increased to 0.987 and 0.801 on the trainingand test period, respectively. Additionally, the model was able to detect and generate one alarm for each incident period that occurred under the test period.The conclusion is that ML can successfully be utilized to detect faults at an earlier stage and potentially circumvent otherwise costly unplanned maintenance. Nevertheless, there is still a lot of room for improvements in the model and the collection of the data. / I hopp om att öka identifieringsgraden av störningar i kraftvärmepannor och därigenom minska oplanerat underhåll konstrueras och evalueras tre maskininlärningsmodeller.Algoritmerna; k-Nearest Neighbor, One-Class Support Vector Machine, och Autoencoder har bevisad framgång inom forskning av anomalidetektion, men är relativt outforskade för industriella applikationer som denna på grund av svårigheten att samla in icke-artificiell uppmärkt data inom området.Grundversionerna av k-Nearest Neighbor och Auto-encoder presterade nästan likvärdigt. Dock var Auto-encoder-modellen lite bättre och nådde ett AUPRC-värde av 0.966 respektive 0.615 på träningsoch testperioden. Inget tillräckligt bra resultat nåddes med One-Class Support Vector Machine. Auto-encoder-modellen gjordes mer sofistikerad för att se hur mycket prestandan kunde ökas. Det visade sig att AUPRC-värdet kunde ökas till 0.987 respektive 0.801 under träningsoch testperioden. Dessutom lyckades modellen identifiera och generera ett larm vardera för alla incidenter under testperioden. Slutsatsen är att ML framgångsrikt kan användas för att identifiera störningar iett tidigare skede och därigenom potentiellt kringgå i annat fall dyra oplanerade underhåll. Emellertid finns det fortfarande mycket utrymme för förbättringar av modellen samt inom insamlingen av data.