| 1. |
- Rahman, Moksadur, 1989-, et al.
(författare)
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A review on the modeling, control and diagnostics of continuous pulp digesters
- 2020
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Ingår i: Processes. - : MDPI AG. - 2227-9717. ; 8:10, s. 1-26
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Tidskriftsartikel (refereegranskat)abstract
- Being at the heart of modern pulp mills, continuous pulp digesters have attracted much attention from the research community. In this article, a comprehensive review in the area of modeling, control and diagnostics of continuous pulp digesters is conducted. The evolution of research focus within these areas is followed and discussed. Particular effort has been devoted to identifying the state-of-the-art and the research gap in a summarized way. Finally, the current and future research directions in the areas have been analyzed and discussed. To date, digester modeling following the Purdue approach, Kappa number control using model predictive controllers and health index-based diagnostic approaches by utilizing different statistical methods have dominated the field. While the rising research interest within the field is evident, we anticipate further developments in advanced sensors and integration of these sensors for improving model prediction and controller performance; and the exploration of different AI-based approaches will be at the core of future research.
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| 2. |
- Rahman, Moksadur, 1989-, et al.
(författare)
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An Approach For Feedforward Model Predictive Control For Pulp and Paper Applications : Challenges And The Way Forward
- 2017
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Ingår i: Paper Conference and Trade Show, PaperCon 2017. - : TAPPI Press. - 9781510847286 ; , s. 1441-1450
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Konferensbidrag (refereegranskat)abstract
- Due to the naturally varying feedstock, significant residence time, insufficient measurements and complex nature of the delignification process, producing pulp with consistent quality i.e. stable kappa number with sufficiently high yield is a challenging task that requires multi-variable process control. A wide variety of control structures, ranging from classical concepts like cascade control, feedforward, ratio control, and parallel control to more modern concepts like model-based predictive control, is used in pulp and paper industries all over the world. In this paper, a survey of model-based predictive control will be presented along with the control challenges that lie within the chemical pulping process. The potential of this control concept for overcoming the aforementioned technical challenges will also be discussed in the second part of the paper. Particular focus will be given on the use of near-infrared spectroscopy based soft-sensors coupled with dynamic process models as an enabler for feedforward model-based predictive control. Overall, the proposed control concept is expected to significantly improve process performance, in the presence of measurement noise and various complex chemical process uncertainties common in pulp and paper applications.
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| 3. |
- Rahman, Moksadur, 1989-, et al.
(författare)
-
An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters
- 2019
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Ingår i: Processes. - : MDPI AG. - 2227-9717. ; 7:9, s. 602-622
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Tidskriftsartikel (refereegranskat)abstract
- Kappa number variability at the continuous digester outlet is a major concern for pulp and paper mills. It is evident that the aforementioned variability is strongly linked to the feedstock wood properties, particularly lignin content. Online measurement of lignin content utilizing near-infrared spectroscopy at the inlet of the digester is paving the way for tighter control of the blow-line Kappa number. In this paper, an innovative approach of feedforwarding the lignin content to a model predictive controller was investigated with the help of modeling and simulation studies. For this purpose, a physics-based modeling library for continuous pulp digesters was developed and validated. Finally, model predictive control approaches with and without feedforwarding the lignin measurement were evaluated against current industrial control and proportional-integral-derivative (PID) schemes.
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| 4. |
- Rahman, Moksadur, 1989-, et al.
(författare)
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Model based Control and Diagnostics strategies for a Continuous Pulp Digester
- 2018
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Ingår i: Paper Conference and Trade Show, PaperCon 2018. - 9781510871892 ; , s. 136-147
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Konferensbidrag (refereegranskat)abstract
- Kappa number, which essentially indicates the amount of lignin left in the pulp after cooking, is the most important physical quantity linked to the quality and economics of a Kraft-pulp mill. Controlling the Kappa number is a difficult task mainly due to the naturally varying feedstock, significant residence time, insufficient measurements and complex nature of the delignification process. Moreover, faults such as screen clogging, hang-ups and channeling in the process often occur and increase the operational costs considerably. In this work, the possibility of feedforwarding the lignin content of incoming wood chips, by a near-infrared spectroscopic measurement of one of the major process disturbances, to a model predictive controller, is investigated by means of modeling and simulation studies. Additionally, a simple Bayesian network based diagnostics approach is proposed to detect the continuous digester faults.
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| 5. |
- Rahman, Moksadur, 1989-
(författare)
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On a learning system for industrial automation : Model-based control and diagnostics for decision support
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Access to energy is fundamental to economic and technological advancement. Hence, the more the world develops, the greater the demand for energy becomes. Evidently, the production and consumption of energy alone account for more than 80% of global anthropogenic greenhouse gas (GHG) emissions. There is broad scientific consensus that efficiency improvements in energy production and consumption must come first on the path to reducing global GHG emissions. As the largest producer and consumer of energy, the industrial sector faces tremendous challenges due to stringent environmental regulations, intense price-based global competition, rising operating costs and rapidly changing economic conditions. Therefore, increasing energy and resource efficiency while improving throughput and asset reliability is a matter of utmost importance. Satisfying such demanding objectives requires an optimal operation, control and monitoring of plant assets and processes. This is one of the main driving forces behind developing digital solutions, methods, and frameworks that can be integrated with old and new industrial automation platforms. The main focus of this dissertation is to investigate frameworks, process models, soft sensors, control optimization, and diagnostic techniques to improve the operation, control, and monitoring of industrial plants and processes. In this thesis, a generic architecture for control optimization, diagnostics, and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies, one representing large-scale assets and another representing a fleet of small-scale assets, are considered to demonstrate the genericness of the proposed system architecture. In this thesis, a very energy-intensive chemical pulping process represents the case study of large-scale assets, and a micro gas turbine (MGT) fleet for distributed heat and power generation represent the case study of a fleet of small-scale assets. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity, and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a MGT fleet. The main argument behind the selection of these two case studies is that, if the proposed learning system architecture can be adapted for these significantly different cases, then it can be adapted for many other industrial applications as well. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control, and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation, and diagnostics techniques are explored for the MGT fleet. Due to the naturally varying wood quality, significant residence time, insufficient measurements, and complexity of pulping reactions, modelling and controlling a continuous pulp digester is a challenging task. Moreover, process abnormalities due to non-ideal flow in the digester often occur that considerably affect the pulp quality. Within this dissertation, variation of wood-chip quality is identified as one of the main process disturbances. Thereafter, a feedforward model predictive control (MPC) approach is explored by feedforwarding the lignin content of the wood chips to the controller. The result shows that the disturbance rejection and tracking performance of the feedforward MPC are superior to other alternatives, like Proportional–integral–derivative (PID), MPC, and current industrial control. When it comes to diagnostics, a literature gap is identified in the area of modelling digester faults. Hence, the well-known Purdue model, a widely used dynamic model of the digester, is extended to simulate process faults like screen-clogging, hangups, and channelling. The findings suggest that both hangups and channelling considerably affect the pulp quality at the blowline. The impact of channelling is prominent on reaction temperature compared to hangups, while hangups change the residence time of the wood chips significantly. Subsequently, a hybrid diagnostics scheme for pulp digester, combining a physical model and a Bayesian network (BN), is demonstrated. Overall, the results show that fault type and severity can be estimated with acceptable accuracy even in presence of noise. Enabling remote fleet diagnostics is expected to foster the commercialization of distributed micro-combined heat and power (micro-CHP) generators, i.e., MGTs. Even though the modelling and diagnostics of large-scale gas turbines are well researched, studies targeting MGT are limited. In this thesis, a physical model of a commercial MGT system is developed. Subsequently, a hybrid scheme by combining a physics-based gas path analysis with a data-driven approach is used to enable MGT diagnostics. The proposed scheme was tested by simulating case studies corresponding to single and multiple faults. Furthermore, sensitivity studies are performed for different measurement uncertainties (i.e., sensor noise and bias) to evaluate the robustness of the scheme against measurement uncertainties. The findings show that the proposed diagnostics approach performs satisfactorily even under measurement uncertainties. To sum up, the increased availability of data and higher computing power is fostering the development of accurate process models and algorithms necessary for optimal operation, control, and monitoring of industrial processes. With the emergence of new measurement techniques, it is possible to leverage productivity and quality with tighter control of key process parameters. Additionally, studying the underlying mechanism of process degradation and developing diagnostics methods by incorporating these can lead to significant economic benefits. Having said that, to tap the full potential of these digital solutions, an integrated framework like that presented in this thesis, i.e., a learning system is essential.
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| 6. |
- Rahman, Moksadur, 1989-
(författare)
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Towards a learning system for process and energy industry : Enabling optimal control, diagnostics and decision support
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Driven by intense competition, increasing operational cost and strict environmental regulations, the modern process and energy industry needs to find the best possible way to adapt to maintain profitability. Optimization of control and operation of the industrial systems is essential to satisfy the contradicting objectives of improving product quality and process efficiency while reducing production cost and plant downtime. Use of optimization not only improves the control and monitoring of assets but also offers better coordination among different assets. Thus, it can lead to considerable savings in energy and resource consumption, and consequently offer a reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks that can be integrated with the existing industrial automation platforms to benefit from optimal control and operation. The main focus of this dissertation is the use of different process models, soft sensors and optimization techniques to improve the control, diagnostics and decision support for the process and energy industry. A generic architecture for an optimal control, diagnostics and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies within the energy-intensive pulp and paper industry and the promising micro-combined heat and power (CHP) industry are selected to demonstrate the learning system. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a micro-CHP fleet. The main argument behind the selection of these two case studies is that if the proposed learning system architecture can be adapted for these significantly different cases, it can be adapted for many other energy and process industrial cases. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation and diagnostics techniques are explored for the micro-CHP fleet.
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| 7. |
- Salman, Chaudhary Awais, 1986-, et al.
(författare)
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Future directions for CHP plants using biomass and waste - Adding production of vehicle fuels
- 2019
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Ingår i: E3S Web of Conferences. - : EDP Sciences. - 2267-1242.
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Konferensbidrag (refereegranskat)abstract
- In Northern Europe, the production of many biobased CHP plants is getting affected due to the enormous expansion of wind and solar power. In addition, heat demand varies throughout the year, and existing CHP plants show less technical performance and suffer economically. By integrating the existing CHP plants with other processes for the production of chemicals, they can be operated more hours, provide operational and production flexibility and thus increase efficiency and profitability. In this paper, we look at a possible solution by converting an existing CHP plant into integrated biorefinery by retrofitting pyrolysis and gasification process. Pyrolysis is retrofitted in an existed CHP plant. Bio-oil obtained from pyrolysis is upgraded to vehicle grade biofuels. Gasification process located upfront of CHP plant provides the hydrogen required for upgradation of biofuel. The results show that a pyrolysis plant with 18 ton/h feed handling capacity (90 MWth), when integrated with gasification for hydrogen requirement and CHP plant for heat can produce 5.2 ton/h of gasoline/diesel grade biofuels. The system integration gives positive economic benefits too but the annual operating hours can impact economic performance.
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| 8. |
- Skvaril, Jan, 1982-, et al.
(författare)
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Fast Determination of Fuel Properties in Solid Biofuel Mixtures by Near Infrared Spectroscopy
- 2017
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Ingår i: Energy Procedia. - Amsterdam : Elsevier. - 1876-6102. ; 105, s. 1309-1317
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Tidskriftsartikel (refereegranskat)abstract
- This paper focuses on the characterization of highly variable biofuel properties such as moisture content, ash content and higher heating value by near-infrared (NIR) spectroscopy. Experiments were performed on different biofuel sample mixtures consisting of stem wood chips, forest residue chips, bark, sawdust, and peat. NIR scans were performed using a Fourier transform NIR instrument, and reference values were obtained according to standardized laboratory methods. Spectral data were pre-processed by Multiplicative scatter correction correcting light scattering and change in a path length for each sample. Multivariate calibration was carried out employing Partial least squares regression while absorbance values from full NIR spectral range (12,000–4000 cm-1), and reference values were used as inputs. It was demonstrated that different solid biofuel properties can be measured by means of NIR spectroscopy. The accuracy of the models is satisfactory for industrial implementation towards improved process control.
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