Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
Tytuł artykułu
Autorzy
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Adsorption cooling and desalination technologies have recently received more attention. Adsorption chillers, using eco-friendly refrigerants, provide promising abilities for low-grade waste heat recovery and utilization, especially renewable and waste heat of the near ambient temperature. However, due to the low coefficient of performance (COP) and cooling capacity (CC) of the chillers, they have not been widely commercialized. Although operating in combined heating and cooling (HC) systems, adsorption chillers allow more efficient conversion and management of low-grade sources of thermal energy, their operation is still not sufficiently recognized, and the improvement of their performance is still a challenging task. The paper introduces an artificial intelligence (AI) approach for the optimization study of a two-bed adsorption chiller operating in an existing combined HC system, driven by low-temperature heat from cogeneration. Artificial neural networks are employed to develop a model that allows estimating the behavior of the chiller. Two crucial energy efficiency and performance indicators of the adsorption chiller, i.e., CC and the COP, are examined during the study for different operating sceneries and a wide range of operating conditions. Thus this work provides useful guidance for the operating conditions of the adsorption chiller integrated into the HC system. For the considered range of input parameters, the highest CC and COP are equal to 12.7 and 0.65 kW, respectively. The developed model, based on the neurocomputing approach, constitutes an easy-to-use and powerful optimization tool for the adsorption chiller operating in the complex HC system.
Rocznik
Tom
Strony
art. no. e137054
Opis fizyczny
Bibliogr. 71 poz., rys., tab.
Twórcy
autor
- Jan Dlugosz University in Czestochowa, Faculty of Science and Technology, ul. A. Krajowej 13/15, 42-200 Czestochowa, Poland
autor
- AGH University of Science and Technology, Faculty of Energy and Fuels, ul. A. Mickiewicza 30, 30-059 Cracow, Poland
autor
- Warsaw University of Technology, Faculty of Power and Aeronautical Engineering, ul. Nowowiejska 24, 00-665 Warsaw, Poland
autor
- AGH University of Science and Technology, Faculty of Energy and Fuels, ul. A. Mickiewicza 30, 30-059 Cracow, Poland
autor
- Jan Dlugosz University in Czestochowa, Faculty of Science and Technology, ul. A. Krajowej 13/15, 42-200 Czestochowa, Poland
autor
- Institute of Fluid-Flow Machinery Polish Academy of Sciences, Department of Distributed Energy, ul. Fiszera 14, 80-952 Gdansk, Poland
autor
- Jan Dlugosz University in Czestochowa, Faculty of Science and Technology, ul. A. Krajowej 13/15, 42-200 Czestochowa, Poland
autor
- AGH University of Science and Technology, Faculty of Energy and Fuels, ul. A. Mickiewicza 30, 30-059 Cracow, Poland
autor
- AGH University of Science and Technology, Faculty of Energy and Fuels, ul. A. Mickiewicza 30, 30-059 Cracow, Poland
autor
- Institute of Fluid-Flow Machinery Polish Academy of Sciences, Department of Distributed Energy, ul. Fiszera 14, 80-952 Gdansk, Poland
Bibliografia
- [1] S. Moser and S. Lassacher, “External use of industrial waste heat – An analysis of existing implementations in Austria”, J. Clean Prod. 264, 121531 (2020).
- [2] J. Krzywanski, K. Grabowska, F. Herman, P. Pyrka, M. Sosnowski, T. Prauzner, and W. Nowak, “Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks”, Energy Conv. Manag. 153, 313‒322 (2017).
- [3] B. Rezaie and M.A. Rosen, “District heating and cooling: Review of technology and potential enhancements”, Appl. Energy 93, 2‒10 (2012).
- [4] A.P. Roskilly and M. Ahmad Al-Nimr, “Sustainable Thermal Energy Management”, Energy Conv. Manag. 159, 396‒397 (2018).
- [5] H. Lund, S. Werner, R. Wiltshire, S. Svendsen, J.E. Thorsen, F. Hvelplund, and B.V. Mathiesen, “4th Generation District Heating (4GDH): Integrating smart thermal grids into future sustainable energy systems”, Energy 68, 1‒11 (2014).
- [6] M. Widziński, P. Chaja, A. Andersen, M. Jaroszewska, S. Bykuć, and J. Sawicki, “Simulation of an alternative energy system for district heating company in the light of changes in regulations of the emission of harmful substances into the atmosphere”, Int. J. Sustain. Energy Plan. Manag. 24, 43‒56 (2019).
- [7] M. Chorowski and P. Pyrka, “Modelling and experimental investigation of an adsorption chiller using low-temperature heat from cogeneration”, Energy 92, 221‒229 (2015).
- [8] R. AL-Dadah, S. Mahmoud, E. Elsayed, P. Youssef, and F. Al-Mousawi, “Metal-organic framework materials for adsorption heat pumps”, Energy 190, 116356 (2020).
- [9] M. Sosnowski, “Evaluation of Heat Transfer Performance of a Multi-Disc Sorption Bed Dedicated for Adsorption Cooling Technology”, Energies 12, 4660 (2019).
- [10] A.S. Alsaman, A.A. Askalany, K. Harby, and M.S. Ahmed, “Performance evaluation of a solar-driven adsorption desalination-cooling system”, Energy 128, 196‒207 (2017).
- [11] A. Kulakowska, A. Pajdak, J. Krzywanski, K. Grabowska, A. Zylka, M. Sosnowski, M. Wesolowska, K. Sztekler, and W. Nowak, “Effect of Metal and Carbon Nanotube Additives on the Thermal Diffusivity of a Silica Gel-Based Adsorption Bed”, Energies 13, 1391 (2020).
- [12] J. Ling-Chin, H. Bao, Z. Ma, W. Taylor, and A. Paul Roskilly, “State-of-the-Art Technologies on Low-Grade Heat Recovery and Utilization in Industry”, in Energy Conversion – Current Technologies and Future Trends, eds. I.H. Al-Bahadly, IntechOpen, 2019.
- [13] K. Grabowska, J. Krzywanski, W. Nowak, and M. Wesolowska, “Construction of an innovative adsorbent bed configuration in the adsorption chiller – Selection criteria for effective sorbent-glue pair”, Energy 151, 317‒323 (2018).
- [14] K. Grabowska, M. Sosnowski, J. Krzywanski, K. Sztekler, W. Kalawa, A. Zylka, and W. Nowak, “The Numerical Comparison of Heat Transfer in a Coated and Fixed Bed of an Adsorption Chiller”, J. Therm. Sci. 27, 421‒426 (2018).
- [15] I.H. Al-Bahadly, Energy Conversion – Current Technologies and Future Trends, London, 2019.
- [16] J. Krzywanski, K. Grabowska, M. Sosnowski, A. Zylka, K. Sztekler, W. Kalawa, T. Wójcik, and W. Nowak, “An Adaptive Neuro-Fuzzy model of a Re-Heat Two-Stage Adsorption Chiller”, Therm. Sci. 23, 1053‒1063 (2019).
- [17] K.J. Chua, S.K. Chou, W.M. Yang, and J. Yan, “Achieving better energy-efficient air conditioning – A review of technologies and strategies”, Appl. Energy 104, 87‒104 (2013).
- [18] X.H. Li, X.H. Hou, X. Zhang, and Z.X. Yuan, “A review on development of adsorption cooling—Novel beds and advanced cycles”, Energy Conv. Manag. 94, 221‒232 (2015).
- [19] K. Sztekler, W. Kalawa, L. Mika, J. Krzywanski, K. Grabowska, M. Sosnowski, W. Nowak, T. Siwek, and A. Bieniek, “Modeling of a Combined Cycle Gas Turbine Integrated with an Adsorption Chiller”, Energies 13, 515 (2020).
- [20] Y.I. Aristov, I.S. Glaznev, and I.S. Girnik, “Optimization of adsorption dynamics in adsorptive chillers: Loose grains configuration”, Energy 46, 484‒492 (2012).
- [21] I.S. Girnik, A.D. Grekova, L.G. Gordeeva, and Yu.I. Aristov, “Dynamic optimization of adsorptive chillers: Compact layer vs. bed of loose grains”, Appl. Therm. Eng. 125, 823‒829 (2017).
- [22] U. Bau, N. Baumgärtner, J. Seiler, F. Lanzerath, C. Kirches, and A. Bardow, “Optimal operation of adsorption chillers: First implementation and experimental evaluation of a nonlinear model-predictive-control strategy”, Appl. Therm. Eng. 149, 1503‒1521 (2019).
- [23] M.B. Elsheniti, M.A. Hassab, and A.-E. Attia, “Examination of effects of operating and geometric parameters on the performance of a two-bed adsorption chiller”, Appl. Therm. Eng. 146, 674‒687 (2019).
- [24] J. Krzywanski, K. Grabowska, M. Sosnowski, A. Żyłka, K. Sztekler, W. Kalawa, T. Wójcik, and W. Nowak, “Modeling of a re-heat two-stage adsorption chiller by AI approach”, MATEC Web Conf. 240, 1‒3 (2018).
- [25] S. Narayanan, S. Yang, H. Kim, and E.N. Wang, “Optimization of adsorption processes for climate control and thermal energy storage”, Int. J. Heat Mass Transf. 77, 288‒300 (2014).
- [26] I.I. El-Sharkawy, H. AbdelMeguid, and B.B. Saha, “Towards an optimal performance of adsorption chillers: Reallocation of adsorption/desorption cycle times”, Int. J. Heat Mass Transf. 63, 171‒182 (2013).
- [27] Q.W. Pan, R.Z. Wang, and L.W. Wang, “Comparison of different kinds of heat recoveries applied in adsorption refrigeration system”, Int. J. Refrig. 55, 37‒48 (2015).
- [28] R.P. Sah, B. Choudhury, R.K. Das, and A. Sur, “An overview of modelling techniques employed for performance simulation of low–grade heat operated adsorption cooling systems”, Renew. Sust. Energ. Rev. 74, 364‒376 (2017).
- [29] L. Rutkowski, Computational Intelligence: Methods and Techniques, Springer Science & Business Media (2008).
- [30] J. Szczepański, J. Klamka, K.M. Węgrzyn-Wolska, I. Rojek, and P. Prokopowicz, “Computational Intelligence and Optimization Techniques in Communications and Control”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 181‒184 (2020).
- [31] B. Paprocki, A. Pregowska, and J. Szczepanski, “Optimizing information processing in brain-inspired neural networks”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 225‒233 (2020).
- [32] A. Cichocki, T. Poggio, S. Osowski, and V. Lempitsky, “Deep Learning: Theory and Practice”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 757‒759 (2018).
- [33] T. Poggio and Q. Liao, “Theory I: Deep networks and the curse of dimensionality”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 761‒773 (2018).
- [34] T. Poggio and Q. Liao, “Theory II: Deep learning and optimization”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 775‒787 (2018).
- [35] M. Figurnov, A. Sobolev, and D. Vetrov, “Probabilistic adaptive computation time”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 811‒820 (2018).
- [36] V. Lebedev and V. Lempitsky, “Speeding-up convolutional neural networks: A survey”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 799‒810 (2018).
- [37] S.C. Cagan, M. Aci, B.B. Buldum, and C. Aci, “Artificial neural networks in mechanical surface enhancement technique for the prediction of surface roughness and microhardness of magnesium alloy”, Bull. Pol. Acad. Sci. Tech. Sci. 67(4), 729‒739 (2019).
- [38] I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in ecodesign”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 199‒206 (2020).
- [39] S. Osowski and K. Siwek, “Local dynamic integration of ensemble in prediction of time series”, Bull. Pol. Acad. Sci. Tech. Sci. 67(3), 517‒525 (2019).
- [40] J. Kurek, B. Świderski, S. Osowski, M. Kruk, and W. Barhoumi, “Deep learning versus classical neural approach to mammogram recognition”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 831‒840 (2018).
- [41] Q. Zhao, Y. Qiu, G. Zhou, and A. Cichocki, “Comparative study on the classification methods for breast cancer diagnosis”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 841‒848 (2018).
- [42] V. Osin, A. Cichocki, and E. Burnaev, “Fast multispectral deep fusion networks”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 875‒889 (2018).
- [43] J. Jakubowski and J. Chmielińska, “Detection of driver fatigue symptoms using transfer learning”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 869‒874 (2018).
- [44] P. Prokopowicz, D. Mikołajewski, K. Tyburek, and E. Mikołajewska, “Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 191‒198 (2020).
- [45] B. Cieniawska, K. Pentoś, and D. Łuczycka, “Neural modeling and optimization of the coverage of the sprayed surface”, Bull. Pol. Acad. Sci. Tech. Sci. 68(3), 601‒608 (2020).
- [46] Y. Li, B. Zhang, and X. Xu, “Decoupling control for permanent magnet in-wheel motor using internal model control based on back-propagation neural network inverse system”, Bulletin of the Polish Academy of Sciences: Technical Science 66(6), 961‒972 (2018).
- [47] R. Korupczyński and J. Trajer, “Assessment of wind energy resources using artificial neural networks – case study at Łódź Hills”, Bull. Pol. Acad. Sci. Tech. Sci. 67, 115‒124 (2019).
- [48] J. Krzywanski, H. Fan, Y. Feng, A.R. Shaikh, M. Fang, and Q. Wang, “Genetic algorithms and neural networks in optimization of sorbent enhanced H2 production in FB and CFB gasifiers”, Energy Conv. Manag. 171, 1651‒1661 (2018).
- [49] J. Krzywanski, M. Wesolowska, A. Blaszczuk, A. Majchrzak, M. Komorowski, and W. Nowak, “The Non-Iterative Estimation of Bed-to-Wall Heat Transfer Coefficient in a CFBC by Fuzzy Logic Methods”, Procedia Eng. 157, 66‒71 (2016).
- [50] W. Muskała, J. Krzywański, R. Rajczyk, M. Cecerko, B. Kierzkowski, W. Nowak, and W. Gajewski, “Investigation of erosion in CFB boilers”, Rynek Energii 87, 97‒102 (2010).
- [51] W. Muskała, J. Krzywański, R. Sekret, and W. Nowak, “Model research of coal combustion in circulating fluidized bed boilers” Chem. Process Eng. 29, 473‒492 (2008).
- [52] A. Zylka, J. Krzywanski, T. Czakiert, K. Idziak, M. Sosnowski, K. Grabowska, T. Prauzner, and W. Nowak, “The 4th Generation of CeSFaMB in numerical simulations for CuO-based oxygen carrier in CLC system”, Fuel 255, 115776 (2019).
- [53] A. Błaszczuk and J. Krzywański, “A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation”, Arch. Thermodyn. 38, 91‒122 (2017).
- [54] J. Krzywanski, M. Wesolowska, A. Blaszczuk, A. Majchrzak, M. Komorowski, and W. Nowak, “Fuzzy logic and bed-to-wall heat transfer in a large-scale CFBC”, Nt. J. Numer. Methods Heat Fluid Flow 28, 254‒266 (2018).
- [55] Machine learning software, Neural Designer. [Online] https://www.neuraldesigner.com/ (accessed on Jun 11, 2019).
- [56] J. Krzywanski, A. Blaszczuk, T. Czakiert, R. Rajczyk, and W. Nowak, “Artificial intelligence treatment of NOX emissions from CFBC in air and oxy-fuel conditions”, CFB-11: Proceedings of the 11th International Conference on Fluidized Bed Technology, 2014, pp. 619‒624.
- [57] J. Krzywański and W. Nowak, “Neurocomputing approach for the prediction of NOx emissions from CFBC in air-fired and oxygen-enriched atmospheres”, J. Power Technol.97, 75‒84 (2017).
- [58] Z. Salam, J. Ahmed, and B.S. Merugu, “The application of soft computing methods for MPPT of PV system: A technological and status review”, Appl. Energy107, 135‒148 (2013).
- [59] J. Krzywanski, “A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods”, Energies 12, 4441 (2019).
- [60] J. Krzywanski and W. Nowak, “Modeling of heat transfer coefficient in the furnace of CFB boilers by artificial neural network approach”, Int. J. Heat Mass Transf. 55, 4246‒4253 (2012).
- [61] J. Krzywanski and W. Nowak, “Modeling of bed-to-wall heat transfer coefficient in a large-scale CFBC by fuzzy logic approach”, Int. J. Heat Mass Transf. 94, 327‒334 (2016).
- [62] A.K. Kar, “Bio inspired computing – A review of algorithms and scope of applications”, Expert Syst. Appl.59, 20‒32 (2016).
- [63] C.Y. Tso, C.Y.H. Chao, and S.C. Fu, “Performance analysis of a waste heat driven activated carbon based composite adsorbent – Water adsorption chiller using simulation model”, Int. J. Heat Mass Transf. 55, 7596‒7610 (2012).
- [64] L. Yang and W. Wang, “The heat transfer performance of horizontal tube bundles in large falling film evaporators”, Int. J. Refrig. 34, 303‒316 (2011).
- [65] W. Kalawa, K. Grabowska, K. Sztekler, J. Krzywański, M. Sosnowski, S. Stefański, T. Siwek, and W. Nowak, “Progress in design of adsorption refrigeration systems. Evaporators”, EPJ Web Conf. 213, 02035 (2019).
- [66] B.B. Saha, S. Koyama, J.B. Lee, K. Kuwahara, K.C.A. Alam, Y. Hamamoto, A. Akisawa, and T. Kashiwagi, “Performance evaluation of a low-temperature waste heat driven multi-bed adsorption chiller”, Int. J. Multiph. Flow 29, 1249‒1263 (2003).
- [67] J. Jeon, S. Lee, D. Hong, and Y. Kim, “Performance evaluation and modeling of a hybrid cooling system combining a screw water chiller with a ground source heat pump in a building”, Energy 35, 2006‒2012 (2010).
- [68] B.B. Saha, E.C. Boelman, and T. Kashiwagi, “Computer simulation of a silica gel-water adsorption refrigeration cycle – the influence of operating conditions on cooling output and COP”, ASHRAE Trans.: Res. 101, 348‒357 (1995).
- [69] K. Habib, B.B. Saha, A. Chakraborty, S. Koyama, and K. Srinivasan, “Performance evaluation of combined adsorption refrigeration cycles”, Int. J. Refrig. 34, 129‒137 (2011).
- [70] B.B. Saha, S. Koyama, T. Kashiwagi, A. Akisawa, K.C. Ng, and H.T. Chua, “Waste heat driven dual-mode, multi-stage, multibed regenerative adsorption system”, Int. J. Refrig. 26, 749‒757 (2003).
- [71] A. Li, A.B. Ismail, K. Thu, K.C. Ng, and W.S. Loh, “Performance evaluation of a zeolite–water adsorption chiller with entropy analysis of thermodynamic insight”, Appl. Energy 130, 702‒711 (2014).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f3aca6dd-0869-424f-b3af-8885d4581c68