Ústav procesního inženýrství

Browse

Recent Submissions

Now showing 1 - 5 of 37
  • Item
    Hierarchical optimisation model for waste management forecasting in EU
    (SPRINGER, 2022-12-01) Smejkalová, Veronika; Šomplák, Radovan; Pluskal, Jaroslav; Rybová, Kristýna
    The level of waste management varies significantly from one EU state to another and therefore they have different starting position regarding reaching defined EU targets. The forecast of waste production and treatment is essential information for the expected future EU targets fulfilment. If waste treatment does not meet the targets under the current conditions, it is necessary to change waste management strategies. This contribution presents a universal approach for forecasting waste production and treatment using optimisation models. The approach is based on the trend analysis with the subsequent data reconciliation (quadratic programming). The presented methodology also provides recommendations to include the quality of trend estimate and significance of territory in form of weights in objective function. The developed approach also allows to put into context different methods of waste handling and production. The variability of forecast is described by prediction and confidence intervals. Within the EU forecast, the expected demographic development is taken into account. The results show that most states will not meet EU targets with current trend of waste management in time. Presented methodology is developed at a general level and it is a suitable basis for strategic planning at the national and transnational level.
  • Item
    Optimal control of combined heat and power station operation
    (Springer Nature, 2023-09-13) Kůdela, Jakub; Suja, Jerguš; Šomplák, Radovan; Pluskal, Jaroslav; Hrabec, Dušan
    Combined heat and power stations have become one of the most utilized units of district heating systems. These stations usually contain several boilers for burning fossil fuels and renewable resources used for heating up steam, which can be used either for residential and commercial heating or electricity generation. To ensure efficiency, a boiler should either run continuously (for at least a given period) on a power output higher than a given threshold or switch off. The optimal control of the plant operations should combine an efficient setup for the turbine and boilers in operation, reflecting the demand for steam and the price of electricity, and a schedule that describes which boilers should be in operation at a given time. This paper proposes a method for optimal control of combined heat and power station operation for a given time horizon. The method is based on a two-level approach. The lower-level models correspond to finding the optimal setup of the combined heat and power station parameters for an hourly demand for different kinds of steam. The upper-level model corresponds to the optimal schedule of the operations of the individual boilers, which is planned for the entire time horizon. The lower-level model is modeled as a mixed-integer linear programming problem and is solved using parametric programming. A dynamic programming algorithm solves the upper-level model with a rolling horizon. The validity of the proposed method and its computational complexity for different granularity of the time horizon, different ranges of the parameters, varying demand for various kinds of steam, and varying electricity prices are investigated in a case study. The presented approach can be readily applied to other control problems with a similar structure.
  • Item
    Machine Learning Method for Changepoint Detection in Short Time Series Data
    (MDPI, 2023-10-05) Smejkalová, Veronika; Šomplák, Radovan; Rosecký, Martin; Šramková, Kristína
    Analysis of data is crucial in waste management to improve effective planning from both short- and long-term perspectives. Real-world data often presents anomalies, but in the waste management sector, anomaly detection is seldom performed. The main goal and contribution of this paper is a proposal of a complex machine learning framework for changepoint detection in a large number of short time series from waste management. In such a case, it is not possible to use only an expert-based approach due to the time-consuming nature of this process and subjectivity. The proposed framework consists of two steps: (1) outlier detection via outlier test for trend-adjusted data, and (2) changepoints are identified via comparison of linear model parameters. In order to use the proposed method, it is necessary to have a sufficient number of experts’ assessments of the presence of anomalies in time series. The proposed framework is demonstrated on waste management data from the Czech Republic. It is observed that certain waste categories in specific regions frequently exhibit changepoints. On the micro-regional level, approximately 31.1% of time series contain at least one outlier and 16.4% exhibit changepoints. Certain groups of waste are more prone to the occurrence of anomalies. The results indicate that even in the case of aggregated data, anomalies are not rare, and their presence should always be checked.
  • Item
    Using numerical dissipation rate and viscosity to assess turbulence-related data accuracy - Part 1: Experimental setup
    (Wiley-VCH GmbH, 2023-08-22) Turek, Vojtěch; Jegla, Zdeněk; Dohnal, Miloslav; Reppich, Marcus
    This is the first part of a two-part paper focusing on the assessment of accuracy of turbulence-related data from computational fluid dynamics (CFD) simulations using effective numerical dissipation rate and effective numerical viscosity. Setup of the CFD cases replicating a swirling pipe flow experiment from literature, for which turbulence-related data measured via laser Doppler anemometry (LDA) had been reported, is presented. The way effective numerical dissipation rate and effective numerical viscosity were obtained for each mesh cell is also discussed. The results of the study are presented in the second part of this series.
  • Item
    Using numerical dissipation rate and viscosity to assess turbulence-related data accuracy - Part 2: Results
    (Wiley-VCH GmbH, 2023-08-22) Turek, Vojtěch; Jegla, Zdeněk; Dohnal, Miloslav; Reppich, Marcus
    This is the second part of a two-part paper focusing on the assessment of accuracy of turbulence-related data from CFD simulations using effective numerical dissipation rate and effective numerical viscosity. Experimental setup has been discussed in the first part of this series. Here, the relevant solution data obtained via CFD are compared to the values from laser Doppler anemometry measurements, and it is studied whether the accuracy of such data can be assessed using the two mentioned quantities. The overall outcome is that although judging mesh quality generally is possible, alone the two quantities are insufficient to draw conclusions regarding the actual solution data.