Dr. Marcin Sosnowski

Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 42-200 Czestochowa, Poland

Dr. Marcin Sosnowski

Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 42-200 Czestochowa, Poland

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Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 July 2024 in Materials

Journal Article

Advanced Computational Methods for Modeling, Prediction and Optimization—A Review

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Agnieszka Kijo-Kleczkowska

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: “Computational Methods: Modeling, Simulations, and Optimization of Complex Systems”; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions.

1 December 2017 in Energy Conversion and Management

Journal Article

Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks

Jaroslaw Krzywanski

Karolina Grabowska

F. Herman

P. Pyrka

Marcin Sosnowski

W. Nowak

14 December 2020 in Energies

Journal Article

Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)

The paper introduces the artificial intelligence (AI) approach for modeling fluidized adsorption beds. The idea of fluidized bed application allows a significantly increased heat transfer coefficient between adsorption bed and the surface of a heat exchanger, improving the performance of adsorption cooling and desalination systems. The Long Short-Term Memory (LSTM) network algorithm was used, classified as a deep learning method, to predict the vapor mass quantity in the adsorption bed. The research used an LSTM network with two hidden layers. The network used in the study is composed of seven inputs (absolute pressures in the adsorption chamber and evaporator, the temperatures in adsorption chamber and evaporator, relative pressure, the temperatures in the center of adsorption bed and 25 mm from the bed center, the kind of the solids mixture, the percentage value of the addition) and one output (mass of the sorption bed). The paper presents numerical research concerning mass prediction with the algorithm mentioned above for three sorbents in fixed ad fluidized beds. The results obtained by the developed algorithm of the LSTM network and the experimental tests are in good agreement of the matching the results above 0.95.

15 March 2017 in E3S Web of Conferences

Conference Paper

Polyhedral meshing as an innovative approach to computational domain discretization of a cyclone in a fluidized bed CLC unit

Marcin Sosnowski

Jaroslaw Krzywanski

Renata Gnatowska

E3S Web of Conferences, open access proceedings in environment, energy and earth sciences

24 February 2022 in Energies

Journal Article

CO₂ Capture by Virgin Ivy Plants Growing Up on the External Covers of Houses as a Rapid Complementary Route to Achieve Global GHG Reduction Targets

Jaroslaw Krzywanski

Waqar Muhammad Ashraf

Global CO₂ concentration level in the air is unprecedently high and should be rapidly and significantly reduced to avoid a global climate catastrophe. The work indicates the possibility of quickly lowering the impact of changes that have already happened and those we know will happen, especially in terms of the CO₂ emitted and stored in the atmosphere, by implanting a virgin ivy plant on the available area of walls and roofs of the houses. The proposed concept of reducing CO₂ from the atmosphere is one of the technologies with significant potential for implementation entirely and successfully. For the first time, we showed that the proposed concept allows over 3.5 billion tons of CO₂ to be captured annually directly from the atmosphere, which makes even up 6.9% of global greenhouse gas emissions. The value constitutes enough high CO₂ reduction to consider the concept as one of the applicable technologies allowing to decelerate global warming. Additional advantages of the presented concept are its global nature, it allows for the reduction of CO₂ from all emission sources, regardless of its type and location on earth, and the fact that it will simultaneously lower the air temperature, contribute to oxygen production, and reduce dust in the environment.

31 October 2022 in Energies

Journal Article

Modelling of SO₂ and NO_x Emissions from Coal and Biomass Combustion in Air-Firing, Oxyfuel, iG-CLC, and CLOU Conditions by Fuzzy Logic Approach

Chemical looping combustion (CLC) is one of the most advanced technologies allowing for the reduction in CO₂ emissions during the combustion of solid fuels. The modified method combines chemical looping with oxygen uncoupling (CLOU) and in situ gasification chemical looping combustion (iG-CLC). As a result, an innovative hybrid chemical looping combustion came into existence, making the above two technologies complementary. Since the complexity of the CLC is still not sufficiently recognized, the study of this process is of a practical significance. The paper describes the experiences in the modelling of complex geometry CLC equipment. The experimental facility consists of two reactors: an air reactor and a fuel reactor. The paper introduces the fuzzy logic (FL) method as an artificial intelligence (AI) approach for the prediction of SO₂ and NO_x (i.e., NO + NO₂) emissions from coal and biomass combustion carried out in air-firing; oxyfuel; iG-CLC; and CLOU conditions. The developed model has been successfully validated on a 5 kW_th research unit called the dual fluidized bed chemical looping combustion of solid fuels (DFB-CLC-SF).

About Me

Associate Professor Marcin Sosnowski after graduating from the Faculty of Electrical Engineering at the Czestochowa University of Technology in 2001 focused on numerical modelling and simulation of flow processes with combustion. He analysed various techniques, prepared customized procedures, and implemented them into open-source codes. He also gained practical skills and engineering know-how by supervising and operating a combined heat and power-generating unit. After obtaining PhD degree in Mechanical Engineering, he started working in the Engineering Design Center, which is the R&D centre of General Electric, where he was responsible for the development and implementation of an innovative IT tool designed for the automatic discretization of computational domain describing combustion chambers of aircraft turbine engines and gas turbines. In 2009, he started working at the Faculty of Sciences and Technology of Jan Dlugosz University in Czestochowa, where he was the deputy director and director of the Institute, vice dean and dean of the Faculty of Science and Technology. He participated in several projects financed by external funds as a manager or coordinator. He has published 100+ scientific papers as an author or co-author and he reviewed 150+ scientific papers. He was a member of the scientific committee or organizing committee of 30+ conferences and served as topic editor, guest editor or member of the editorial office of 20+ journals.

Dr. Marcin Sosnowski

Dr. Marcin Sosnowski

Research Focus & Metrics

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About Me