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  • 1.
    Aslanidou, Ioanna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Soibam, Jerol
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Comparison of machine learning approaches for spectroscopy applications2022In: Proceedings of the 63rd International Conference of Scandinavian Simulation Society / [ed] Lars O. Nord; Tiina Komulainen; Corinna Netzer; Gaurav Mirlekar; Berthe Dongmo-Engeland; Lars Eriksson, 2022, p. 80-85Conference paper (Refereed)
    Abstract [en]

    In energy production the characterization of the fuel is a key aspect for modelling and optimizing the operation of a power plant. Near-infrared spectroscopy is a wellestablished method for characterization of different fuels and is widely used both in laboratory environments and in power plants for real-time results. It can provide a fast and accurate estimate of key parameters of the fuel, which for the case of biomass can include moisture content, heating value, and ash content. These instruments provide a chemical fingerprint of the samples and require a calibration model to relate that to the parameters of interest.

    A near-infrared spectrometer can provide point data whereas a hyperspectral imaging camera allows the simultaneous acquisition of spatial and spectral information from an object. As a result, an installation above a conveyor belt can provide a distribution of the spectral data on a plane. This results in a large amount of data that is difficult to handle with traditional statistical analysis. Furthermore, storage of the data becomes a key issue, therefore a model to predict the parameters of interest should be able to be updated continuously in an automated way. This makes hyperspectral imaging data a prime candidate for the application of machine learning techniques. This paper discusses the modelling approach for hyperspectral imaging, focusing on data analysis and assessment of machine learning approaches for the development of calibration models.

  • 2.
    Helmryd Grosfilley, Emil
    et al.
    Uppsala University, Sweden.
    Robertson, Gustav
    Uppsala University, Sweden.
    Soibam, Jerol
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Le Corre, Jean-Marie
    Westinghouse Electric Sweden AB, Sweden.
    Investigation of Machine Learning Regression Techniques to Predict Critical Heat Flux over a Large Parameter Space2023In: Proceedings 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20) Washington, D.C., August 20-25, 2023, 2023, p. 4516-4529Conference paper (Refereed)
    Abstract [en]

    A unifying and accurate model to predict Critical Heat Flux (CHF) over a wide range of conditions has been elusive since wall boiling research emerged. With the release of the data utilized in the development of the 2006 Groeneveld CHF lookup table (LUT), by far the most extensive public CHF database available to date (nearly 25000 data points), development of data-driven predictions models over a large parameter space in simple geometry (vertical, uniformly heated round tubes) can be performed. Furthermore, the popularization of machine learning techniques to solve regression problems has led to more advanced tools for analyzing large and complex databases. This work compares three machine learning algorithms to predict the entire LUT CHF test database. For each selected regression algorithm (ν-Support vector, Gaussian process, and neural network), an optimized hyperparameter set is fitted. The best-performing algorithm is the neural network, which can achieve a standard deviation of the predicted/measured factor of 12.3%, three times lower than the LUT. In comparison, the Gaussian process regression and the ν-Support vector regression achieve a standard deviation of 17.7%, about two times lower than the LUT. All considered algorithms hence significantly outperform the LUT prediction performance. The neural network model and training methodology are designed to prevent overfitting, which is confirmed by data analysis of the predictions. Finally, a feasibility study of transfer learning is presented and future development directions (including uncertainty quantification) are discussed. 

  • 3.
    Kyprianidis, Konstantinos
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Karlsson, Mikael
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Tryzell, Robert
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Soibam, Jerol
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ševcik, Martin
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    On-line Powerplant Control using Near-InfraRed Spectroscopy: OPtiC-NIRS, REPORT 2021:7462021Report (Other academic)
    Abstract [en]

    Near InfraRed Spectroscopy (NIRS) offers rapid on-line analysis of biomass feedstocks and can be utilized for process control of biomass- based combined heat and power plants. Within the OPtiC-NIRS project we have carried out a full-scale on-site testing of different NIRS for online powerplant control at the facilities of Mälarenergi and Eskilstuna Strängnäs Energi och Miljö. 

    The project has been focused on developing and testing robust NIRS soft-sensors for fuel higher heating value and composition (incl. moisture, components such as recycle wood and glass, different type of plastics and ash) and combining them with dynamic models for on-line feed-forward process monitoring and control. Expected benefits include reduced risk of agglomeration and pollutant emissions formation as well as improved production control. A longer-term potential and ambition is to be able to identify the fossil content in municipal waste fuel, which can hopefully be addressed in a follow-up study. 

    Download full text (pdf)
    on-line-powerplant-control-using-near-infrared-spectroscopy-energiforskrapport-2021-746
  • 4.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Khalesimoghadam, Seyedpedram
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Soibam, Jerol
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Application of single-point and hyperspectral imaging near-infrared sensors and machine learning algorithms for real-time biomass characterization2019Conference paper (Refereed)
    Abstract [en]

    Biomass is typically a material with highly variable properties making its use in industrial combustion processes challenging due to requirements on the steady operation. Property such as moisture content has an impact on fuel ignition characteristics and heat release from the biomass. Ash content negatively influences fluidization of the boiler bed and after-burning of small fuel particles, by forming an impermeable layer on the surface resulting in incomplete combustion and formation of harmful emissions.

    The large variability of the properties thus creates undesired process instabilities which need to be addressed in a timely manner by appropriate operational/regulatory measures adjusting e.g. fluidization velocity, distribution of combustion air, under-pressure in the furnace etc. Consequently, there is a need for the implementation of sensors able to measure the properties of interest in real-time. In our previous studies, we demonstrated the ability of a single-point near-infrared sensor to measure fuel properties in real-time in a laboratory environment. However, we found that there is limited representativeness of the single-point measurements as also a cross-sectional variation of the fuel properties on the conveyor belt was apparent.

    Therefore, the implementation of a sensor able to measure also a spatial distribution of the material in the biomass stream is suggested. Literature review shows that it can be achieved by the implementation of a near-infrared hyperspectral imaging camera.

    The aim of the work is to present research activities at the Future Energy Center, Mälardalen University leading towards the installation of a) single-point and b) hyperspectral imaging near-infrared sensors for real-time moisture and ash content measurements. The study further presents the concept of NIR sensors integration for process optimization and the introduction of new advanced control concepts for steam boilers.

    Download full text (pdf)
    fulltext
  • 5.
    Soibam, Jerol
    Mälardalen University, School of Business, Society and Engineering.
    Machine Learning Techniques for Enhanced Heat Transfer Modelling2024Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    With the continuous growth of global energy demand, processes from power generation to electronics cooling become vitally important. The role of heat transfer in these processes is crucial, facilitating effective monitoring, control, and optimisation. Therefore, advancements and understanding of heat transfer directly correlate to system performance, lifespan, safety, and cost-effectiveness, and they serve as key components in addressing the world's increasing energy needs.

    The field of heat transfer faces the challenge of needing intensive studies while retaining fast computational speeds to allow for system optimisation. While advancements in computational power are significant, current numerical models lack in handling complex physical problems such as ill-posed. The domain of heat transfer is rapidly evolving, driven by a wealth of data from experimental measurements and numerical simulations. This data influx presents an opportunity for machine learning techniques, which can be used to harness meaningful insights about the underlying physics.

    Therefore, the current thesis aims to the explore machine learning methods concerning heat transfer problems. More precisely, the study looks into advanced algorithms such as deep, convolutional, and physics-informed neural networks to tackle two types of heat transfer: subcooled boiling and convective heat transfer. The thesis further addresses the effective use of data through transfer learning and optimal sensor placement when available data is sparse, to learn the system behaviour. This technique reduces the need for extensive datasets and allows models to be trained more efficiently. An additional aspect of this thesis revolves around developing robust machine learning models. Therefore, significant efforts have been directed towards accounting for the uncertainty present in the model, which can further illuminate the model’s behaviour. This thesis shows the machine learning model's ability for accurate prediction. It offers insights into various parameters and handles uncertainties and ill-posed problems. The study emphasises machine learning's role in optimising heat transfer processes. The findings highlight the potential of synergistic application between traditional methodologies and machine learning models. These synergies can significantly enhance the design of systems, leading to greater efficiency.

    Download full text (pdf)
    fulltext
  • 6.
    Soibam, Jerol
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bel Fdhila, Rebei
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Hitachi Energy Research, Västerås, Sweden..
    Inverse flow prediction using ensemble PINNs and uncertainty quantification2024In: International Journal of Heat and Mass Transfer, ISSN 0017-9310, E-ISSN 1879-2189, Vol. 226Article in journal (Refereed)
    Abstract [en]

    The thermal boundary conditions in a numerical simulation for heat transfer are often imprecise. This leads to poorly defined boundary conditions for the energy equation. The lack of accurate thermal boundary conditions in real-world applications makes it impossible to effectively solve the problem, regardless of the advancement of conventional numerical methods. 

    This study utilises a physics-informed neural network to tackle ill-posed problems for unknown thermal boundaries with limited sensor data. The network approximates velocity and temperature fields while complying with the Navier-Stokes and energy equations, thereby revealing unknown thermal boundaries and reconstructing the flow field around a square cylinder. The method relies on optimal sensor placement determined by the QR pivoting technique, which ensures the effective capture of the dynamics, leading to enhanced model accuracy. In an effort to increase the robustness and generalisability, an ensemble physics-informed neural network is implemented. This approach mitigates the risks of overfitting and underfitting while providing a measure of model confidence. As a result, the ensemble model can identify regions of reliable prediction and potential inaccuracies. Therefore, broadening its applicability in tackling complex heat transfer problems with unknown boundary conditions.

  • 7.
    Soibam, Jerol
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bel Fdhila, Rebei
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Hitachi Energy Research, Västerås, Sweden.
    Inverse Flow Prediction Using Pinns In An Enclosure Containing Heat Sources2023In: Proc. Thermal Fluids Eng. Summer Conf., Begell House Inc. , 2023, p. 429-438Conference paper (Refereed)
    Abstract [en]

    While simulating heat transfer problems using a numerical method, the thermal boundary conditions are never known precisely, which leads to ill-posed boundary conditions for the energy equation. The lack of knowledge of accurate thermal boundary conditions in a practical application makes it impossible to solve this problem no matter how sophisticated the conventional numerical method is. Hence, the current work addresses this ill-posed problem using physics informed neural network by assuming that the thermal boundary near the source is unknown and only a few measurements of temperature are known in the domain. Physics-informed neural network is employed to represent the velocity and temperature fields, while simultaneously enforcing the Navier-Stokes and energy equations at random points in the domain. This work serves as an inverse problem since the goal here is to reproduce the global flow field and temperature profile in the domain with few measurement data points. Furthermore, the work focuses on using transfer learning for different parameters such as the position and size of the source term inside the enclosure domain. These parameters are of particular interest while designing a thermal system and being able to predict the flow and thermal behaviour instantly will allow for better design of the system. For this study, the sensors' data are extracted from numerical simulation results. The placement of the sensors in the domain plays a vital role in accuracy hence, sensors were optimized using the residual of the energy equation. The results obtained from this work demonstrate that the proposed method is in good agreement with the underlying physics represented by the numerical results.

  • 8.
    Soibam, Jerol
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Rabhi, Achref
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bel Fdhila, Rebei
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model2020In: Energies, E-ISSN 1996-1073, Vol. 13, no 22, article id 5987Article in journal (Refereed)
    Abstract [en]

    Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime.

  • 9.
    Soibam, Jerol
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Rabhi, Achref
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bel Fdhila, Rebei
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Hitachi ABB Powergirds, Power Grids Research, Sweden.
    PREDICTION OF THE CRITICAL HEAT FLUX USING PARAMETRIC GAUSSIAN PROCESS REGRESSION2021In: Proceedings of the 15th International Conference on Heat Transfer, Fluid Mechanics andThermodynamics (HEFAT2021), HEFAT , 2021, p. 1865-1870Conference paper (Refereed)
    Abstract [en]

    A sound understanding of the critical heat flux is of prime importance for any industrial boiling system design and safety. From the literature, the majority of the critical heat flux studies are based on empirical knowledge, often supported by ex- perimental investigations which are performed under specific conditions difficult to be generalized. Consequently, most of the available correlations have ±30% predictive error when com- pared to measurement data. Hence, accurate prediction of this quantity remains an open challenge for the thermal engineering community. The present study aims to investigate the hidden features that exist in experimental data using a machine learning technique. Firstly, a literature survey is carried out to collect experimental data for boiling flows in tubes under low pressure and low flow conditions. These experimental data consist of the following parameters: system pressure, mass flux, characteristic dimensions, thermodynamic quality, inlet subcooling, and critical heat flux. A parametric Gaussian process regression model is used to predict the critical heat flux. The prediction obtained from the model is then compared with experimental measurements and the values obtained from the critical heat flux look-up table. The model used in this study is capable of predicting the critical heat flux with better accuracy along with the information of prediction uncertainty. Moreover, it provides insights on the relevance of the different input parameters to the prediction of the critical heat flux and aligns well with the underlying physics. The model used in this study shows a good level of robustness which can be further extended for other geometries, datasets, and operating conditions. 

    Download full text (pdf)
    Prediction_Of_Critical_Heat_Flux_Using_Parametric_Gaussian_Process_Regression_HEFAT_2021
  • 10.
    Soibam, Jerol
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Scheiff, Valentin
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bel Fdhila, Rebei
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Hitachi Energy Res, Vasteras, Sweden .
    Application of deep learning for segmentation of bubble dynamics in subcooled boiling2023In: International Journal of Multiphase Flow, ISSN 0301-9322, E-ISSN 1879-3533, Vol. 169, article id 104589Article in journal (Refereed)
    Abstract [en]

    The present work focuses on designing a robust deep-learning model to track bubble dynamics in a vertical rectangular mini-channel. The rectangular mini-channel is heated from one side with a constant heat flux, resulting in the creation of bubbles. Images of the bubbles are recorded using a high-speed camera, which serve as the input data for the deep learning model. The raw image data acquired from the high-speed camera is inherently noisy due to the presence of shadows, reflections, background noise, and chaotic bubbles. The objective is to extract the mask of the bubble given all these challenging factors. Transfer learning is adopted to eliminate the need for a large dataset to train the deep learning model and also to reduce computational costs. The trained model is then validated against the validation datasets, demonstrating an accuracy of 98% while detecting the bubbles. The model is then evaluated on different experimental conditions, such as lighting, background, and blurry images with noise. The model demonstrates high robustness to different conditions and is able to detect the edges of the bubbles and classify them accurately. Moreover, the model achieves an average intersection over union of 85%, indicating a high level of accuracy in predicting the masks of the bubbles. The method enables accurate recognition and tracking of individual bubble dynamics, capturing their coalescence, oscillation, and collisions to estimate local parameters by proving the bubble masks. This allows for a comprehensive understanding of their spatial-temporal behaviour, including the estimation of local Reynolds numbers.

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