Combustion monitoring for biomass boilers using multivariate image analysis

Les procédés de combustion sont utilisés dans la plupart des industries chimiques, métallurgiques et manufacturières, pour produire de la vapeur (chaudières), pour sécher des solides ou les transformer dans des fours rotatifs (ou autres). Or, les combustibles fossiles qui les alimentent (ex. : gaz naturel) sont de plus en plus dispendieux, ce qui incite plusieurs compagnies à utiliser d’autres sources de combustibles tels que de la biomasse, des rejets inflammables produits par le procédé lui-même ou des combustibles fossiles de moindre qualité. Ces alternatives sont moins coûteuses, mais de composition, et donc de pouvoir calorifique, plus variable. De telles variations dans la chaleur dégagée par la combustion perturbent l’opération des procédés et la qualité des produits qui dépendent de ces installations. De nouvelles stratégies de contrôle de la combustion doivent donc être élaborées afin de tenir compte de cette nouvelle réalité. Il a été récemment démontré que l’énergie dégagée par la combustion est corrélée à l’aspect visuel de la flamme, principalement sa couleur, ce qui permet d’en quantifier les variations par imagerie numérique. L’objectif de ce projet industriel consiste à faire la démonstration que l’analyse d’images multivariées peut servir à l’identification du comportement d’une chaudière à biomasse. La chaudière à biomasse opérée par Irving Pulp & Paper Ltd (Saint-John, Nouveau-Brunswick) fera office d’exemple. Les résultats montrent qu’un modèle bâtit à partir des informations fournies par les images ainsi que les données de procédé donne de bonnes prédictions de la quantité de vapeur produite (R2modèle=93.6%, R2validation=70.1%) et ce, 2,5 minutes à l’avance. Ce projet est la première étape du développement d’une nouvelle stratégie de contrôle automatique de la combustion de biomasse, capable de stabiliser l’énergie dégagée, malgré les variations imprévisibles dans le pouvoir calorifique et les proportions des combustibles utilisés provenant de différentes sources.Biomass is increasingly used in the process industry, particularly in utility boilers, as a low cost source of renewable, carbon neutral energy. It is, however, a solid fuel with some degree of moisture which feed rate and heat of combustion is often highly variable and difficult to control. Indeed, the variable bark properties such as its carbon content or its moisture content have an influence on heat released. Moreover, the uncertain and unsteady bark flow rate increases the level of difficulty for predicting heat released. The traditional 3-element boiler control strategy normally used needs to be improved to make sure the resulting heat released remains as steady as possible, thus leading to a more widespread use biomass as a combustible. It has been shown in the past that the flame digital images can be used to estimate the heat released by combustion processes. Therefore, this work investigates the use of Multivariate Image Analysis (MIA) of biomass combustion images for early detection of combustion disturbances. Applied to a bark boiler operated by Irving Pulp & Paper Ltd, it was shown to provide good predictions, 2.5 minutes in advance, of variations in steam flow rate (R2fit=93.6%, R2val=70.1%) when information extracted from images were combined with relevant process data. This project is the first step in the development of a new automatic control scheme for biomass boilers, which would have the ability to take proactive control actions before such disturbances in the manipulated variable (i.e. bark flow and bark properties) could affect steam production and steam header pressure

Flame stability and burner condition monitoring through optical sensing and digital imaging

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for flame stability and burner condition monitoring on fossil-fuel-fired furnaces. A review of methodologies and technologies for the monitoring of flame stability and burner condition is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating optical sensing, digital imaging, digital signal/image processing and soft computing techniques, is proposed. Based on this strategy, a prototype flame imaging system is developed. The system consists of a rigid optical probe, an optical-bearn-splitting unit, an embedded photodetector and signal-processing board, a digital camera, and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and radiation signals. Power spectral density, oscillation frequency, and a proposed universal flame stability index are used for the assessment of flame stability. Kernel-based soft computing techniques are employed for burner condition monitoring. Specifically, kernel principal components analysis is used for the detection of abnormal conditions in a combustion process, whilst support vector machines are used for the prediction of NO x emission and the identification of flame state. Extensive experimental work was conducted on a 9MW th heavy-oil-fired combustion test facility to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 660MWth heavy-oil-fired boiler to investigate the cause of the boiler vibration from a flame stability point of view. Results Obtained from the tests are presented and discussed

계산 과학적 접근을 통한 지속가능한 공정의 최적 설계 및 산업에의 응용

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부, 2018. 2. 이원보.Recently, in the field of chemical engineering, many types of research based on high-performance computing have been combined with computer-aided process systems engineering. Therefore, various techniques of computational science such as computational fluid dynamics, optimization methodology, and machine learning have been applied to the problems of chemical reactor modeling and process optimization. Notably, in this advance computational science approach, the scope of research extends to non-traditional fields such as reactive research according to the 3D shape of the reactor that has not been easily solved in the past and surrogate model based optimization using machine learning. In this thesis, various methods are proposed to obtain the maximum profit with minimum cost by making a breakthrough design. In parallel, there is a growing demand for sustainable chemical processes in chemical engineering. Conventional chemical processes are highly dependent on oil prices, and unless a diverse portfolio is designed, the sustainability of their chemical industries can be violated because of the oil controlling from the Middle East or US. In addition, these crude oil based chemical processes and power plants generate a great deal of CO2. Therefore, it is not necessary to capture these CO2 and make only meaningless storage but to reproduce it as a product that can be used and make it economical carbon capture, utilization, and storage (CCUS) technology. To solve this series of processes, the Gas-to-Liquid (GTL) process and CCUS are being researched and developed in various ways. In this thesis, I will discuss the process modeling, optimizing, and designing the reactor and process using CFD, mathematical programming, machine learning, deep learning, and derivative-free optimization techniques in computational science. First of all, the Fischer-Tropsch microchannel reactor and 3-phase carbonation reactor, which are the key reactor of two most important processes of the sustainable process, the gas-to-liquid process (GTL) and the carbon capture, utilization, and storage (CCUS), are modeled by CFD. Also, we propose an integration platform of CFD model and process simulator and conduct research from the point of view of combining with existing process engineering. With these advanced reactor model, we propose a multi-objective optimization methodology using a stochastic optimization algorithm, a genetic algorithm (GA) with e-constraint method for simultaneously maximizing C5+ productivity and minimizing the temperature rise of a Fischer-Tropsch microchannel reactor. The main mixed integer nonlinear programming (MINLP) optimization problem is decomposed into an external CFD reactor model function and internal optimization constraints. The methodology is applied to the catalyst packing zone division, which is divided and packed with a different dilution ratio to distribute the heat of reaction evenly. The best solutions of the proposed optimizer are reproducible with different crossover fractions and are more efficient than other traditional non-convex constraint local solvers. Based on the Pareto optimal solution of the final optimizer with 4 zones, discrete dilution increases C5+ productivity to 22% and decreases ∆Tmax to 63.2% compared to the single zone catalyst packing case. Finally, several Pareto optimal solutions and sub-optimal solutions are compared and the results are documented in terms of C5+ productivity and maximum temperature increase. In process scale optimization platform, a modified DIRECT algorithm with a sub-dividing step for considering hidden constraints is proposed. The effectiveness of the algorithm is exemplified by its application to a cryogenic mixed refrigerant process using a single mixed refrigerant for natural gas liquefaction and its comparison with a well-known stochastic algorithm (GA, PSO, SA), and model based search algorithm (SNOBFIT), local solver (GPS, GSS, MADS, active-set, interior-point, SQP), and other hidden constraint handling methods, including the barrier approach and the neighborhood assignment strategy. Optimal solution calculated by the proposed algorithms decreases the specific power required for natural gas liquefaction to 18.9% compared to the base case. In the same chapter, heat exchanger network synthesis (HENS) has progressed by using mathematical programming-based simultaneous methodology. Although various considerations such as non-isothermal mixing and bypass streams are applied to consider real world alternatives in modeling phase, many challenges are faced because of its properties within non-convex mixed-integer nonlinear programming (MINLP). We propose a modified superstructure, which contains a utility substage for use in considering multiple utilities in a simultaneous MINLP model. To improve model size and convergence, fixed utility locations according to temperature and series connections between utilities are suggested. The numbers of constraints, discrete, and continuous variables show that overall model size decreases compared with previous research. Thus, it is possible to expand the feasible search area for reaching the nearest global solution. The models effectiveness and applications are exemplified by several literature problems, where it is used to deduce a network superior to that of any other reported methodology. In the case of plant-wide scale systems, a non-linear surrogate model based on deep learning is proposed using a variational autoencoder with deep convolutional layers and a deep neural network with batch normalization (VAEDC-DNN) for real-time analysis of the probability of death (Pdeath). VAEDC can extract representation features of the Pdeath contour with complicated urban geometry in the latent space, and DNN maps the variable space into the latent space for the Pdeath image data. The chlorine gas leak accident in the Mipo complex (city of Ulsan, Republic of Korea) is used for verification of the model. The proposed model predicts the Pdeath image within a mean squared error of 0.00246, and compared with other models, it exhibits superior performance. Furthermore, through the smoothness of image transition in the variable space, it is confirmed that image generation is not overfitting by data memorization. Finally, a pilot scale (1.0 BPD) compact GTL process comprising of reforming section, CO2 separating section and Fischer -Tropsch (FT) synthesis section is presented. Systematic design procedure adopted for the design of a modular 0.5 BPD microchannel FT reactor block design consisting of 528 process channels is described. On average 98.27% CH4 conversion to syngas in reforming section comprising of a pre-reformer unit and a tri-reformer unit, CO2 separation rate of 36.75 % along with CO/H2 reduction from 2.67 to 2.08 in CO2 membrane separation section comprising of three membrane separators, for the entire plant operation duration of 450 hr demonstrated successful and stable operation of pre-processing sections of the present pilot-scale compact GTL process. Parallel operation of FT microchannel reactor and multitubular fixed bed type FT reactor proved failure for latter due to reaction runaway, while the former showed stable operation with high CO conversion of 83% and successful temperature control (at 220 oC, 230 oC and at 240 oC during the 139 hr operation), which demonstrated the appreciable performance of KOGAS-SNU novel microchannel FT reactor. Furthermore, a tank agitator carbonation reactor in which the reaction between calcium oxide and carbon dioxide takes place is studied to understanding that how 6 design variables (the number of impeller, impeller type, D/T, clearance, speed, baffle) affect to the solid dispersion using CFD simulation.CHAPTER 1. Introduction 17 1.1. Research motivation 17 1.1.1. Chronological stages of development of process design 19 1.1.2. Current status of process systems engineering with computational science approach 21 1.1.3. Introduction to the sustainable process 23 1.2. Research objectives 25 1.3. Outline of the thesis 26 1.4. Associated publications 29 CHAPTER 2. Study of the Novel Reactor Models using Computational Science 30 2.1. Introduction 30 2.2. Gas-to-Liquid (GTL) Fischer-Tropsch (FT) reactor model 32 2.2.1. 2D axisymmetric computational fluid dynamics (CFD) based Fischer-Tropsch microchannel reactor single-channel model 37 2.2.2. 3D CFD based Fischer-Tropsch microchannel reactor multi-channel model 52 2.3. Carbon Capture, Utilization, and Storage (CCUS) multiphase carbonation reactor model 75 2.3.1. Rigorous reaction kinetics for carbonation based CCUS reactor 77 2.3.2. Eulerian multiphase model for carbonation reactor 92 2.4. CFD-Process integrated platform for simultaneous process and reactor design 105 2.4.1. Introduction 105 2.4.2. Model formulation 106 2.4.3. Result and discussion 112 2.4.4. Conclusion 116 CHAPTER 3. Optimization for the Unit, Process, and Plant-wide Systems 117 3.1. Introduction 117 3.2. Reactor systems scale optimization 119 3.2.1. Multi-objective optimization of microchannel reactor for Fischer-Tropsch synthesis using computational fluid dynamics and genetic algorithm 119 3.3. Process systems scale optimization 152 3.3.1. A modified DIRECT algorithm for hidden constraints optimization problem 152 3.3.2. Simultaneous synthesis of a heat exchanger network with multiple utilities using utility substages 200 3.4. Plant-wide systems scale modeling and optimization 233 3.4.1. Toxic gas release modeling for real-time analysis using variational autoencoder with convolution neural networks 233 CHAPTER 4. Industrial Applications 276 4.1. Optimal Design and Operation of Fischer-Tropsch Microchannel Reactor for Pilot Scale Compact Gas-to-Liquid Process 276 4.1.1. Pilot scale compact GTL process 277 4.1.2. Microchannel FT reactor design 286 4.1.3. Pilot plant experiment 287 4.1.4. Result and discussion 291 4.1.5. Conclusion 304 4.2. Industrial scale (40 tonCO2/day) CCUS carbonation reactor geometry design optimization 306 4.2.1. Design procedure and simulation set-up 310 4.2.2. Result and Discussion 313 4.2.3. Conclusion 332 CHAPTER 5. Concluding Remarks 334 5.1. Summary of Contributions 334 5.2. Future Work 337 Nomenclature 340 Reference 346 Abstract in Korean (국문초록) 360Docto

Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

The research landscape on the applications of advanced computational tools (ACTs) such as machine/deep learning and neural network algorithms for energy and power generation (EPG) was critically examined through publication trends and bibliometrics data analysis. The Elsevier Scopus database and the PRISMA methodology were employed to identify and screen the published documents, whereas the bibliometric analysis software VOSviewer was used to analyse the co-authorships, citations, and keyword occurrences. The results showed that 152 documents have been published on the topic comprising conference proceedings (58.6%) and articles (41.4%) between 2004 and 2022. Publication trends analysis revealed the number of publications increased from 1 to 31 or by 3,000% over the same period, which was ascribed to the growing scientific interest and research impact of the topic. Stakeholder analysis revealed the top authors/researchers are Anvari M, Ghaderi SF and Saberi M, whereas the most prolific affiliation and nations actively engaged in the topic are the North China Electric Power University, and China, respectively. Conversely, the top funding agency actively backing research on the topic is the National Natural Science Foundation of China (NSFC). Co-authorship analysis revealed high levels of collaboration between researching nations compared to authors and affiliations. Hotspot analysis revealed three major thematic focus areas namely; Energy Grid Forecasting, Power Generation Control, and Intelligent Energy Optimization. In conclusion, the study showed that the application of ACTs in EPG is an active, multidisciplinary, and impact area of research with potential for more impactful contributions to research and society at large

Marine Power Systems

Marine power systems have been designed to be a safer alternative to stationary plants in order to adhere to the regulations of classification societies. Marine steam boilers recently achieved 10 MPa pressure, in comparison to stationary plants, where a typical boiler pressure of 17 MPa was the standard for years. The latest land-based, ultra-supercritical steam boilers reach 25 MPa pressure and 620 °C temperatures, which increases plant efficiency and reduces fuel consumption. There is little chance that such a plant concept could be applied to ships. The reliability of marine power systems has to be higher due to the lack of available spare parts and services that are available for shore power systems. Some systems are still very expensive and are not able to be widely utilized for commercial merchant fleets such as COGAS, mainly due to the high cost of gas turbines. Submarine vehicles are also part of marine power systems, which have to be reliable and accurate in their operation due to their distant control centers. Materials that are used in marine environments are prone to faster corrosive wear, so special care also should be taken in this regard. The main aim of this Special Issue is to discuss the options and possibilities of utilizing energy in a more economical way, taking into account the reliability of such a system in operation

XVIII International Coal Preparation Congress

Changes in economic and market conditions of mineral raw materials in recent years have greatly increased demands on the ef fi ciency of mining production. This is certainly true of the coal industry. World coal consumption is growing faster than other types of fuel and in the past year it exceeded 7.6 billion tons. Coal extraction and processing technology are continuously evolving, becoming more economical and environmentally friendly. “ Clean coal ” technology is becoming increasingly popular. Coal chemistry, production of new materials and pharmacology are now added to the traditional use areas — power industry and metallurgy. The leading role in the development of new areas of coal use belongs to preparation technology and advanced coal processing. Hi-tech modern technology and the increasing interna- tional demand for its effectiveness and ef fi ciency put completely new goals for the University. Our main task is to develop a new generation of workforce capacity and research in line with global trends in the development of science and technology to address critical industry issues. Today Russia, like the rest of the world faces rapid and profound changes affecting all spheres of life. The de fi ning feature of modern era has been a rapid development of high technology, intellectual capital being its main asset and resource. The dynamics of scienti fi c and technological development requires acti- vation of University research activities. The University must be a generator of ideas to meet the needs of the economy and national development. Due to the high intellectual potential, University expert mission becomes more and more called for and is capable of providing professional assessment and building science-based predictions in various fi elds. Coal industry, as well as the whole fuel and energy sector of the global economy is growing fast. Global multinational energy companies are less likely to be under state in fl uence and will soon become the main mechanism for the rapid spread of technologies based on new knowledge. Mineral resources will have an even greater impact on the stability of the economies of many countries. Current progress in the technology of coal-based gas synthesis is not just a change in the traditional energy markets, but the emergence of new products of direct consumption, obtained from coal, such as synthetic fuels, chemicals and agrochemical products. All this requires a revision of the value of coal in the modern world economy

Environmental Analysis of Organic Pollutants

In recent decades, the environmental impact of organic pollutants, especially persistent and emerging organic pollutants, has attracted widespread attention, and related research has been rapidly developed. Organic pollutants represent a popular topic of research in the environmental field. Large amounts of organic pollutants, which are widely distributed in air, water, sediment, soil, and other environmental media, are created via industrial production and other human activities. A series of research projects have been carried out regarding the development of analytical methods for organic pollutants, the distribution of environmental media, environmental concentration, environmental fate, the exploration of new pollutants, and the non-target analysis of organic pollutants

Actions for Bioenergy and Biofuels: A Sustainable Shift

The topic of bioenergy is a multidisciplinary one, where the use of resources and skills can be optimized for the development of sustainable models. It is a time for green strategies, but also for action. It is, therefore, necessary to implement projects that address virtuous examples of the circular bioeconomy. All politicians are called on to contribute, because this global goal can only be achieved if a contribution is made by all countries

Biomass Processing for Biofuels, Bioenergy and Chemicals

Biomass can be used to produce renewable electricity, thermal energy, transportation fuels (biofuels), and high-value functional chemicals. As an energy source, biomass can be used either directly via combustion to produce heat or indirectly after it is converted to one of many forms of bioenergy and biofuel via thermochemical or biochemical pathways. The conversion of biomass can be achieved using various advanced methods, which are broadly classified into thermochemical conversion, biochemical conversion, electrochemical conversion, and so on. Advanced development technologies and processes are able to convert biomass into alternative energy sources in solid (e.g., charcoal, biochar, and RDF), liquid (biodiesel, algae biofuel, bioethanol, and pyrolysis and liquefaction bio-oils), and gaseous (e.g., biogas, syngas, and biohydrogen) forms. Because of the merits of biomass energy for environmental sustainability, biofuel and bioenergy technologies play a crucial role in renewable energy development and the replacement of chemicals by highly functional biomass. This book provides a comprehensive overview and in-depth technical research addressing recent progress in biomass conversion processes. It also covers studies on advanced techniques and methods for bioenergy and biofuel production