Linköping Electronic Conference Proceedings
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Dynamic Modelling and Simulation of Raw Meal Calcination for Isothermal Boundary Conditions
Get PDFThis article describes modelling and simulation of heating and calcination of raw meal particles. The purpose is to determine the time required to obtain a certain calcination degree for particles that are exposed to surroundings with a specified temperature. The impact of applying different reactor temperature values and different particle sizes is investigated. The aggregated calcination degree as a function of time is calculated for a typical raw meal with a specified particle size distribution and with different contents of CaCO3 in different size classes. The developed model can be used as a basis for determining the required size of potential new calciner reactor types
Modeling of Artificial Snow Production Using Annular Twin-fluid Nozzle
Get PDFNedsnødd AS develops a system for generating artificial snow. The concept is to optimize snow production for geographical locations where so-called ‘marginal’ conditions for snow production dominate the weather picture. To produce artificial snow, liquid water in a spray is exposed to cold air and becomes an agglomerate of frozen droplets. The basic idea is to improve the atomization of water to enhance the snow production capability. This work develops a model for the cooling process of the water droplets to simulate the processes determining the capabilities for snow production equipment
Increasing Interpretability and Prediction Rate by combining Self-organizing Maps with Modeling Algorithms
Get PDFWe consider supervised learning problems, for which we need not only the accurate model, but also the model, that explains the relation between inputs and a target variable. There are modeling problems, when production experts can measure their confidence in the modeling results by modeling metrics, such as accuracy, but need an explanation for what was the reason of desirable or undesirable situation or system state in the past. In this study we utilize a combination of self-organizing maps and multiple linear modeling to increase the interpretability and accuracy. We assume that the target variable can be explained differently by different patterns that characterizes inputs data. By solving clustering problem for subset of inputs, we have structured data and can relate each cluster to its representative or cluster profile, which explains the cluster. Based on that structure we build linear model for each cluster dataset, and coefficients of this model explain the influence of factors for particular inputs characteristics. To cut the number of inputs we use L1-regularization for linear model. Proposed approach was tested on several industry related problems and implemented in application
Developing a Dynamic Diesel Engine Model for Energy Optimal Control
Get PDFA dynamic heavy-duty Euro 6 diesel engine model for energy optimal control is developed. The modeling focus is on accuracy in the entire engine operating range, with attention to the region of highest efficiency and physically plausible extrapolation. The effect of the air-to-fuel ratio on combustion efficiency is studied, and it is demonstrated how this influences the energy optimal transient control. A convenient, physics-based, method for pressure sensor bias estimation is also presented
Accurate Simulation for Numerical Optimal Control
Get PDFAccurate simulation of the numerical optimal control in software environments where call to simulation routines is explicit, for instance Matlab and SciPy. A discussion on the simulation aspects of numerical optimal control, how it may fail, and how such erroneous results can be detected using accurate simulation. The key contribution is how to accurately include a piecewise constant control input in the simulations, which is discussed in detail, including code examples. The technique is demonstrated on an example problem which show how simulation can be used to analyze optimal control problems with uncertainty, but also demonstrates how erroneous simulation may lead to erroneous conclusions
Bearing Defect and Misalignment Diagnostics using Local Regularity and Sparse Frequency Analysis
Get PDFA local regularity signal can be estimated from a vibration measurement with the help of the continuous wavelet transform (CWT). The resulting local regularity signal contains a lot of diagnostic information about different faults states of a machine. It is also typically a sparse signal and thus not well suited for frequency analysis using the discrete Fourier transform (DFT). In this paper, the frequency analysis of the local regularity signal is performed using the Lomb-Scargle periodogram. Another possibility is to use the methods of compressed sensing. Vibration measurements from different fault states from test rigs are utilized in validating the proposed method and comparing it with other methods. The induced fault conditions include a bearing inner ring defect and misalignment of a claw clutch. The results are compared to more traditional spectra calculated directly from the vibration measurement, such as the spectrum of the squared envelope
Modeling and Simulation for Decision making in Sustainable and Resilient Assembly System Selection
Get PDFResiliency requires manufacturing system adaptability to internal and external changes, such as quick responses to customer needs, supply chain disruptions, and markets changes, while still controlling costs and quality. Sustainability requires simultaneous consideration of the economic, environmental, and social implications associated with the production and delivery of goods. Due to increasing complexity, the engineering of a production system is a knowledge-intensive process. In this paper, a summary of system adaptation methods are shown, and a holistic methodology for the assembly equipment and system modeling and evaluation is explained. The aim here is to bring resiliency and sustainability considerations into the early decision-making process. The methodology is based on estimations on system performance, using discrete event simulation run results, or other process modeling methods, and the use of Key Performance Indicators (KPI), such as Overall Equipment Efficiency (OEE), connected to cost parameters and environmental aspects analysis. Overall, it is a tool developed through multiple projects for design specification reviews and improvements, trade-off analysis, and investments justification
Automated Cost Optimization of CO2 Capture Using Aspen HYSYS
Get PDFCO2 can be captured by absorption into monoethanol amine (MEA) followed by desorption. In this work, three configurations; standard, vapour recompression and a simple split-stream (rich split) have been simulated with an equilibrium-based model in Aspen HYSYSTM V10.0 using flue gas data from a natural gas based power plant. Adjust and recycle blocks available in Aspen HYSYS are used to automate the energy and material balance for a specified configuration. Optimization can be performed by minimizing the total cost calculated in an Aspen HYSYS spreadsheet. The equipment cost was obtained from Aspen In-plant Cost EstimatorTM V10.0, and an enhanced detailed factor (EDF) method was used to estimate the total investment cost. Parametric studies of absorber packing height, minimum approach temperature in the main heat exchanger, flash pressure and split ratio were performed at 85% capture efficiency for the three configurations. The calculated cost optimum process parameters for the standard process were 15 m packing height and 13 °C minimum approach temperature. For the vapor recompression case, a flash pressure of 150 kPa provided the lowest total cost. The calculated optimum rich split ratio was 12%. Automated calculations are dependent on stable convergence of the simulations. A specific challenge is the adjustment of the amine recirculation to obtain a specified total capture rate
A Model of Aerobic and Anaerobic Metabolism in Cancer Cells – Parameter Estimation, Simulation, and Comparison with Experimental Results
Get PDFWe present a mathematical model of metabolism in cancer cells that is capable of describing both aerobic oxidative metabolism and anaerobic fermentation metabolism, and how cancer cells shift between these metabolic states when exposed to different substrates and different enzymatic inhibitors. The model is designed to be used in combination with experimental data gathered with an Agilent Seahorse XF metabolic analyzer. The model is parameterized in a manual tuning procedure to fit experimental data, and validated against experimental data from another setup, to which the model shows good conformity. We also investigate the structural identifiability of the model. The results indicate that the model is structurally identifiable, and that it can thus be uniquely parameterized, using the following 5 measurements: extracellular concentrations of glucose, glutamine and lactate, proton production rate (a Seahorse XF analyzer measurement) and oxygen consumption rate
Comparison and Application of Multi-Rate Methods for Real-Time Simulations of Production Systems
Get PDFA distributed simulation makes it possible to couple simulation tools and lays the foundation for the usage of multicore capabilities to decrease the calculation time. In consequence the simulation is partitioned on multiple simulation tasks. If simulation tasks with different integration step sizes are used, the configuration is called multi-rate simulation. In real-time simulations the tasks are calculated parallely, which means that fast tasks do not wait for the simulation result of slower tasks. A sequential approach where fast tasks wait for slower tasks would slow down the overall simulation and therefore tear the real-time requirements. In a real-time multi-rate approach, signal processing of the coupling signals between the tasks is required. For this signal processing, multi-rate methods are used. Easy multi-rate methods lead to stepped signals in the faster task, because the slower task does not provide a new calculated signal at each timestep of the faster task. In this work further methods are investigated in an industrial real-time simulation environment. The analysis contains continuous and discontinuous as well as energy conserving methods. It is shown how these different methods perform for various kinds of signals. The methods are compared and evaluated on signals with different characteristics, which allows a recommendation for the choice of a method in a specific simulation scenario. The application of the multi-rate methods is shown on an example virtual commissioning simulation of an industrial robot. It shows that the right choice of a multi-rate method has a big impact on the overall simulation result