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Digital twins have emerged as a critical technology to enable smart production. Digital twins can enhance the current production system by optimizing the current setup and facilitating decision-making based on facts rather than gut feeling. Despite the numerous benefits explored, digital twins have faced many challenges in developing and implementing production systems. Their complexity is causing a lack of digital twin implementations in the production system. This complexity can be traced back to physical and virtual entities and the digital twin development process. By conducting a case study in a global manufacturing company, this publication explores the sources of complexity when developing digital twins. The findings are organized around the digital twin development steps and their corresponding complexity. The number of different types of entities being modeled, the choice of the modeling approach, modeling low-frequency events, emergent phenomena, and the unpredictability and variability of the manufacturing process are all examples of structural and dynamic complexity that have been found to impede success in digital twin applications. This research has implications for managers who are involved in the development of digital twins in their organizations. It can help with methodological guidance when dealing with an undefined and complicated process of digital twin development.

Leveraging the potential of digital twins is of utmost importance to support smart production. Digital twin research has principally focused on defining digital twin concepts and applications and proposing various frameworks for their implementation. Less is known about using simplified models to overcome many challenges related to digital twin models. Based on a longitudinal case study at a multinational manufacturing company engaged in digital twins in manufacturing efforts, this paper identifies the main challenges encountered related to people, processes, and technology, as well as requirements placed on a digital twin. This study also presents the opportunities of applying simplified models for digital twins to overcome the identified challenges and fulfill the defined requirements. The present study provides theoretical and practical implications of the development of digital twins in manufacturing, focusing attention on the challenges and requirements that affect the outcome of the manufacturing company to drive digital twin efforts.

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.

Nowadays thermal management becomes a challenge as it implies high power density with high lossesconverted to large heat release. For low power levels, natural or forced single-phase convection could besufficient. For a much higher heat release nucleate boiling can be the alternative solution since it can dissipate the heat more efficiently, thanks to the latent heat effect present during the phase change. Its performance depends on many parameters that enable potential control and make system integration often very complex. The transition towards nucleate boiling, called Onset of Nucleate Boiling requires better estimation, and the mechanism still lacks understanding, especially in mini-channels. This study aims to characterize nucleate boiling in a rectangular mini-channel experimental set-up, built at Mälardalenuniversity, to better characterize the onset of nucleate boiling and the fully developed bubbly flow. The experiment allows full control of single-phase and two-phase regimes by varying the thermo-hydraulic and heat transfer conditions. With the use of a high-speed camera, bubble dynamics and their principal characteristics such as size, shape, propagation, and nucleation site location are determined with a digital image analysis technique developed within this work. The image processing has proved to be successful even on noisy images due to shadows or background changes. The reconstruction of segmented bubbles enabled flexible and automated bubble and path detection with a statistical approach, especially at the Onset of Nucleate Boiling. Local Reynolds numbers are then estimated to determine the drag coefficient in the flow during bubble growth, or their coalescence.

Adequately mixing of reactants is an important factor for efficient deNOx process in power station NOx emission control system. In this study, an experimental validated CFD simulation is conducted to investigate the effect of spray operation conditions on the mixing uniformity of reactant ammonia vapor in deNOx process occurring in a power station's furnace. According to the CFD simulation results, it is found that spray momentum ratio, initial droplet size and initial ammonia concentration all affect the mixing uniformity of ammonia vapor. Overall, a larger spray momentum ratio, larger initial droplet size and lower ammonia concentration contributes positively to the mixing uniformity. By comparing the same spray momentum ratio but different nozzle inlet velocity and furnace inlet velocity, it is found that the impact of spray momentum ratio mainly comes from furnace inlet velocity not nozzle inlet velocity. In addition, gravity should not be neglected. In the end, the method described in this study could provide a systematic way to study the effects of nozzle operation conditions on deNOx process.

The interest in using Two-Phase Loop Thermosyphons(TPLT) for heat recovery and energy saving within different in-dustrial processes has been in rise on the last few decades. Thesedevices are characterized by geometrical flexibility, as well asenhanced heat exchange rates. However, TPLT operation in-volves complex physical mechanisms, where different flow andheat transfer regimes are encountered. These regimes are crucialto be assessed and understood, in order to successfully predictand optimize the TPLT operation. In this paper, a comprehensive one-dimensional thermo-hydraulic modelling approach is developed and presented in or-der to simulate the TPLT operation. The novelty of this modellies in the exhibition of the different experienced complex flowpatterns, heat transfer regimes and physical mechanisms, includ-ing the dry-out prediction and reporting. This modelling frame-work is based on the separated two-fluid model coupled withmass, momentum and energy balances as well as relevant ther-modynamic constraints. The obtained results are compared to theavailable experimental measurements from literature, and a goodagreement is found with a maximum prediction error of 7%. Furthermore, a sensitivity analysis is performed aiming todetermine the effect of the operating saturation temperature, andtherefore the filling ratio, on the average heat transfer coefficientof the TPLT’s evaporator. Optimal values leading to enhance theheat removal are proposed and discussed at the end of this paper.

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.

Hydrocracking is an energy-intensive process, and its control system aims at stable product specifications. When the main product is diesel, the quality measure is usually 95% of the true boiling point. Constant diesel quality is hard to achieve when the feed characteristics vary and feedback control has a long response time. This work suggests a feedforward model predictive control structure for an industrial hydrocracker. A state-space model, an autoregressive exogenous model, a support vector machine regression model, and a deep neural network model are tested in this structure. The resulting reactor temperature decisions and final diesel product quality values are compared against each other and against the actual measurements. The results show the importance of the feed character measurements. Significant improvements are shown in terms of product quality as well as energy savings through decreasing the heat duty of the preheating furnace.

This paper describes an investigation into the off-design performance comparison of single and two-shaft gas turbine engines. A question that has been asked for a long time is which gas turbine delivers a better thermal efficiency at part load. The authors, notwithstanding their intensive searches, were unable to find a comprehensive answer to this question. A detailed investigation was carried out using a state-of-the-art performance evaluation method and the answer was found to be: It depends! In this work, the performance of two engine configurations is assessed. In the first one, the single-shaft gas turbine operates at constant shaft rotational speed. Thus, the shape of the compressor map rotational speed line will have an important influence on the performance of the engine. To explore the implications of the shape of the speed line, two single-shaft cases are examined. The first case is when the speed line is curved and as the compressor pressure ratio falls, the nondimensional mass flow increases. The second case is when the speed line is vertical and as the compressor pressure ratio falls, the nondimensional mass flow remains constant. In the second configuration, the two-shaft engine, the two shafts can be controlled to operate at different rotational speeds and also varying relationships between the rotational speeds. The part-load operation is characterized by a reduction in the gas generator rotational speed. The tool, which was used in this study, is a 0-D whole engine simulation tool, named Turbomatch. It was developed at Cranfield and it is based on mass and energy balance, carried out through an iterative method, which is based on component maps. These generic, experimentally derived maps are scaled to match the design point of a particular engine before an off-design calculation is performed. The code has been validated against experimental data elsewhere, it has been used extensively for academic purposes and the research activities that have take

In order to accommodate more intermittent renewable energy sources, biomass fueled combined heat and power plants (bio-CHPs) can contribute towards sustainable and flexible energy systems. However, the varying properties of biomass, such as moisture contents and heating values, can clearly affect the combustion in boilers, which further affects the flexibility provided by bio-CHPs. In order to achieve better control, this paper proposes a feed-forward model predictive controller (FF MPC) to handle the variation of biomass properties. A dynamic model was built in Dymola to simulate the performance of a bubbling fluidized bed boiler, which was validated against the real operation data. Based on the simulation, the key manipulated variables were optimized for the given controlled variables. The advantages of the proposed FF MPC were demonstrated through comparisons with proportional- integral (PI), FF PI and MPC. The results of FF MPC show the best performance, such as the lowest magnitude of fluctuations for 3 outputs (thermal load, steam and fluidized bed temperature), and the most stable operation. Consequently, FF MPC can potentially increase the electricity generation and further lead to an economic benefit. Using one week in winter as an example, compared to PI, FF PI and MPC, FF MPC can generate more electricity and improve revenues by 14.77 MWh/590 =C, 4.1 MWh/164 =C and 5.03 MWh/211.2 =C respectively.

Electricity grid flexibility is vital for renewable energy to be used effectively. Power-to-gas technologies are investigated to connect electricity grid to gas grid and to tackle capacity challenges. Grid management expenses consist of redispatch and feed-in management. These management procedures, next to being costly, cause a significantenergy loss. Proton-exchange membrane electrolyzer installations were studied to reduce these expenses and recover energy. The change in the levelized cost of hydrogen production with varying electrolyzer capacities was presented. The sensitivity of the levelized cost and net presentvalue with respect to installation costs, maintenance costs, and electricity prices were investigated. While the electricity prices have the most significant effect on the levelized cost of hydrogen production, the net present value was affected considerably by the hydrogen selling price. Possible energy savings were calculated between 2 – 23 GWh for 2, 5, 10, 20 MW installations. The annual gridmanagement expense savings were in the range of 0.2– 2.3 million Euros, increasing with the increasing electrolyzer capacity.

Despite that mechanistic and accurate correlations predicting the Onset of Nucleate Boiling (ONB) for pool boiling are widely presented in the literature, models for forced convective boiling remain few. These models do not provide the desired quality, principally because they do not consider important features of convective boiling. In this work, numerical investigations of the ONB for water boiling flow at atmospheric pressure upward a narrow rectangular channel (3 mm × 100 mm × 400 mm) are carried out based on Computational Fluid Dynamics (CFD) simulations. The predictions of the CFD calculations are validated with the available experimental data. A new ONB model incorporating the convective boiling features is developed and proposed. This model is derived based on several CFD simulation data, covering wide operating conditions. The flow Reynolds number ranges from 959 to 13500, inlet subcooling from 2.5 to 30 K and applied heat flux from 5 to 90 kW/m2. The new model predictions have a standard deviation of 2.7% where its performance is better than ±0.3 K when compared with additional simulation data that are provided for validation. © 2020 Elsevier Ltd, Export Date: 21 December 2020; Article; CODEN: IJHMA; Correspondence Address: Rabhi, A.; Mälardalen Univeristy, Högskoleplan 1, 722 20 Västerås, Sweden; email: achref.rabhi@mdh.se; Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KKS; Funding details: ABB; Funding text 1: The authors gratefully acknowledge ABB AB, Westinghouse Electric Sweden AB, HITACHI ABB Power Grids Sweden and the Swedish Knowledge Foundation (KKS) for their support and would like to particularly thank ABB AB for providing the HPC platform.

The main outcomes of an engineering course should be for the students to achieve the educational goals, enhance their problem solving capabilities and develop essential skills for their future career. In that context, it is important to understand what motivates the students and what helps them develop an engineering mindset. This paper discusses the improvement of a course with the use of student feedback to motivate students and help them develop essential skills. The purpose of the paper is to provide insight into how different aspects of the course are linked to the students’ growth. Different activities have been integrated in the course over the past years. The effect these have on the student motivation to follow the course and develop skills, knowledge and interest in the subject is discussed through the analysis of student performance, student feedback and the experience of the lecturers. The improvements in the course based on the student feedback were received positively by the students, whose learning experience improved, even though the workload of the course was high. Their motivation to successfully complete the course has also increased through the changes in the delivery of the course and the support by the teachers. The combination of student feedback and teacher experience is key for the improvement of a course, while ensuring that the students develop their engineering knowledge. Therefore, the teachers should strike a balance between helping the students find the solution and encouraging them to think on their own in order to develop essential skills.

Model-based approaches are essential for the operation, optimization, and control of applications in the process industry. Different structures are often investigated to build representative and robust models, and a set of parameters with the same attributes are required to utilize them effectively. Parameter estimation gets arduous with the increasing complexity of the process, the model, and the size of the parameter space. In this work, a parameter-estimation problem based on a steady-state model of diesel hydrodesulfurization is investigated using gradient-based and gradient-free optimizers. The optimal parameter sets obtained are then assessed in terms of performance and computational time for the different optimizers. Furthermore, the sensitivity of the various parameters is also investigated. Due to the catalytic reactions in this process, some parameters have to be updated depending on the catalyst activity. In addition to the initial estimation, the updated parameters are also studied, and instead of a time-based one, a tolerance-based recalculation schedule is suggested. Finally, the robustness of the final model is analyzed by giving different operating conditions and feed characteristics. The adaptive parameter approach proved better data fitting capabilities by improving the coefficient of determination for temperature predictions.

The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university mana, FuturE

Crude oil has different characteristics according to its origin, and this difference causes suboptimal operation if not considered. Similar to other refinery operations, hydrodesulfurization suffers from lacking this knowledge. Information on the true boiling point curve of the feed, next to its sulfur concentration, can be used to optimize the operating temperature. In this work, an optimization problem is demonstrated for two manipulated temperatures of the system and solved by using a gradient-based and a gradient-free algorithm. While the gradient based solution has a single objective of minimum sulfur content, the gradient-free solution has three objectives: minimum sulfur, inlet temperature, and secondary hydrogen flow rate. A continuous lumping model is used to predict the temperature and sulfur responses of a real hydrodesulfurization plant. An adaptive approach is preferred for the model to cope with the catalyst deactivation interference on the product sulfur content constraint. The effect of changing feed on optimality is demonstrated by using eight types of feeds with varying true boiling point and sulfur content. In addition to that, the impact of catalyst age is shown on similar feed processed on different dates.

The energy generation landscape is changing, pushed by stricter regulations for emissions control and green energy generation. The limitations of renewable energy sources, however, require flexible energy production sources to supplement them. Micro gas turbine based combined heat and power plants, which are used for domestic applications, can fill this gap if they become more reliable. This can be achieved with the use of an engine monitoring and diagnostics system: real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. In order to allow the step change in the connection of small engines to the grid, a fleet monitoring system for micro gas turbines is required. A proposed framework combines a physics-based model and a data-driven model with machine learning capabilities for predicting system behavior, and includes a purpose-developed diagnostic tool for anomaly detection and classification for a multitude of engines. The framework has been implemented on a fleet of micro gas turbines and some of the lessons learned from the demonstration of the concept as well as key takeaways from the general literature are presented in this paper. The extension of fleet monitoring to optimal operation and production planning in relation to the needs of the grid will allow the micro gas turbines to fit in the future green energy system, connect to the grid, and trade in the energy market. The requirements on the system level for the widespread use of micro gas turbines in the energy system are addressed in the paper. A review of the current solutions in fleet monitoring and diagnostics, generally developed for larger engines, is included, with an outlook into a sustainable future.