Laboratoř integrace procesů


Recent Submissions

Now showing 1 - 5 of 12
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    Review of Developments in Plate Heat Exchanger Heat Transfer Enhancement for Single-Phase Applications in Process Industries
    (MDPI, 2023-07-30) Arsenyeva, Olga; Tovazhnyansky, Leonid; Kapustenko, Petro; Klemeš, Jiří; Varbanov, Petar Sabev
    A plate heat exchanger (PHE) is a modern, effective type of heat transfer equipment capable of increasing heat recuperation and energy efficiency. For PHEs, enhanced methods of heat transfer intensification can be further applied using the analysis and knowledge already available in the literature. A review of the main developments in the construction and exploration of PHEs and in the methods of heat transfer intensification is presented in this paper with an analysis of the main construction modifications, such as plate-and-frame, brazed and welded PHEs. The differences between these construction modifications and their influences on the thermal and hydraulic performance of PHEs are discussed. Most modern PHEs have plates with inclined corrugations on their surface that create a strong, rigid construction with multiple contact points between the plates. The methods of PHE exploration are mostly experimental studies and/or CFD modelling. The main corrugation parameters influencing PHE performance are the corrugation inclination angle in relation to the main flow direction and the corrugation aspect ratio. Optimisation of these parameters is one way to enhance PHE performance. Other methods of heat transfer enhancement, including improving the form of the plate corrugations, use of nanofluids and active methods, are considered. Future research directions are proposed, such as improving fundamental understanding, developing new corrugation shapes and optimisation methods and area and cost estimations.
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    A Bi-level optimization approach to reduce the pollution burden of lake water with ecological compensation
    (Elsevier, 2023-07-01) Linhuan, He; Varbanov, Petar Sabev; Yao, Liming
    Lake Ecological Compensation (LEC) mechanism is a localized approach of payment for ecosystem services to address the conflict between economic development and ecological conservation. However, how to motivate regional stakeholders to participate in water quality protection is a challenge. Based on the traditional Coase Theorem, vertical eco-compensation mechanism has been proposed to solve pollution of lake basin. The quan-tification of LEC is characterized by key water quality indicators (NH3-N and COD) in this paper. Integrating LEC mechanism with the ecological-economic model, this paper proposes a bi-level optimization framework for the conservation of the lake water environment. Referring to Coase's theory, the leader's goal is to distribute waste load permits equally to sub-regions while followers aim to minimize environmental costs. The appropriate application of this method to Taihu Basin demonstrates its efficiency and practicality. The LEC mechanism with different scenarios is analyzed and suggestions for lake water quality are made. The results show that: (1) Considering equitable principle, the new COD allocation scheme has reduced the total amount of emissions by 17% compared to the data in 2020, and the total amount of NH3-N has decreased by 16%. (2) With the coop-eration of lake basin institutions, the LEC mechanism is proved to be an effective measure in promoting the conservation of the lake water environment. (3) The spillover effect of environmental and ecological policies in lake water indicates the need to upgrade industrial structure. This paper proposes to provide a more reliable the conservation of the lake water environment paradigm.
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    Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan
    (MDPI, 2023-06-01) Sadenova, Marzhan; Beisekenov, Nail; Varbanov, Petar Sabev; Pan, Ting
    The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017-2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions.
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    Optimizing plastics recycling networks
    (Elsevier, 2023-06-14) Aviso, Kathleen B.; Baquillas, Jonna C.; Chiu, Anthony S. F.; Jiang, Peng; Fan, Yee Van; Varbanov, Petar Sabev; Klemeš, Jiří; Tan, Raymond R
    Plastic pollution is a serious sustainability issue facing the global community. Fragments of macroplastics and microplastics pollute terrestrial and aquatic ecosystems, while nanoplastics can also degrade air quality. The recent COVID-19 pandemic also exacerbated the problem. Large-scale commercial use of plastics recycling technologies is hindered by various socio-economic barriers. In particular, cross-contamination of mixed plastic streams is prevalent due to imperfect waste segregation. The concept of Plastics Recycling Networks is intro-duced to facilitate planning of reverse supply chains using optimization models. In this work, basic Linear Programming and Mixed-Integer Linear Programming models are developed for matching sources of waste plastic with plastic recycling plants within Plastics Recycling Networks. These models allocate streams while considering the ability of recycling plants to tolerate contaminants. Two illustrative case studies are analyzed to demonstrate the effectiveness of the models, and policy implications for mitigation of plastic pollution are dis-cussed. These models enable planning of networks with some tolerance for contaminants in plastic waste, and can be the basis for developing new variants to handle additional real world aspects.
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    Melamine-isatin tris Schiff base as an efficient corrosion inhibitor for mild steel in 0.5 molar hydrochloric acid solution: weight loss, electrochemical and surface studies
    (Royal Society of Chemistry, 2023-06-22) Arshad, Ifzan; Qureshi, Khizar; Saleemi, Awais Siddique; Abdullah, Ali; Bahajjaj, Aboud Ahmed Awadh; Ali, Shafaqat; Bokhari, Syed Awais Ali Shah
    In the current study, 3,3 & PRIME;,3 & PRIME;& PRIME;-((1,3,5-triazine-2,4,6-triyl)tris(azaneylylidene))tris(indolin-2-one) (MISB), which is the condensation product of melamine (triazine) and isatin, was investigated as a mild steel corrosion inhibitor in 0.5 M HCl. The ability of the synthesized tris-Schiff base to suppress corrosion was evaluated utilizing weight loss measurements, electrochemical techniques and theoretical computation. The maximum inhibition efficiency of 92.07%, 91.51% and 91.60% was achieved using 34.20 x 10(-3) mM of MISB in weight loss measurements, polarization, and EIS tests, respectively. It was revealed that an increase in temperature decreased the inhibition performance of MISB, whereas an increase in the concentration of MISB increased it. The analysis demonstrated that the synthesized tris-Schiff base inhibitor followed the Langmuir adsorption isotherm and was an effective mixed-type inhibitor, but it exhibited dominant cathodic behavior. According to the electrochemical impedance measurements, the R-ct values increased with an increase in the inhibitor concentration. The weight loss and electrochemical assessments were also supported by quantum calculations and surface characterization analysis, and the SEM images showed a smooth surface morphology.