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  Torrefied Biomass Detoxification: Extended Residence Time Overcomes Heating Rate Effects on PCDD/PCDF Formation during Torrefaction

  (bioresources.cnr.ncsu.edu; Department of Forest Biomaterials at North Carolina State University, 2026) Sobol, Łukasz; Conesa, Juan A.; Sabat, Dominika; Telega, Paweł; Zawiślak, Ireneusz; Szufa, Szymon; Dyjakon, Arkadiusz; Energy, Environment and Society Centre; Wrocław University of Environmental and Life Sciences. Sobol, Łukasz.; Department of Chemical Engineering, University of Alicante. Conesa, Juan A.; Faculty of Environmental Science and Technology, Wrocław University of Environmental and Life Sciences. Sabat, Dominika.; Department of Applied Bioeconomy, Wrocław University of Environmental and Life Sciences. Telega, Paweł.; Department of Human Nutrition, Wrocław University of Environmental and Life Sciences. Zawiślak, Ireneusz.; Faculty of Process and Environmental Engineering, Lodz University of Technology. Szufa, Szymon.; Energy, Environment and Society Centre; Wrocław University of Environmental and Life Sciences. Dyjakon, Arkadiusz.

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  Environmental applications of torrefied biomass may be severely limited by contamination with carcinogenic polychlorinated dibenzo-p-dioxins (PCDD), dibenzofurans (PCDF), and dioxin-like biphenyls (dl-PCB). In this article, the first systematic assessment of the influence of heating rate (HR) and residence time (RT) on the evolution of PCDD/PCDF/dl-PCB loads in bark-derived chars produced via torrefaction is presented. Sixteen torrefied biomass variants were examined, produced at a fixed temperature of 260 °C under different HR and RT conditions. Short processing durations (≤ 60 min) were found to induce a twofold increase in torrefied biomass toxicity (up to 1.314 ± 0.197 ng-TEQ·kg-1 88% DM; DM stands for the dry matter), driven by precursor-mediated formation of PCDD/PCDF/dl-PCB, whereas extended residence times were shown to progressively detoxify the torrefied biomass, ultimately restoring toxicity levels comparable with, or markedly below, those of the raw biomass (0.689 ± 0.103 ng-TEQ·kg-1 88% DM). The formation pathways of PCDD/PCDF/dl-PCB were governed by HR, as evidenced by the distinct kinetic profile observed at 30 °C·min-1, reflecting suppression of the precursor-driven pathway, and by the newly identified “delay effect” of toxicity increase at 50 °C·min-1 conditions. Collectively, these findings demonstrate the critical influence of HR and RT on the optimization of torrefaction conditions to produce torrefied biomass with reduced PCDD/PCDF/dl-PCB contents.

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  Ecological, economic and legal determinants of electricity production in the context of co-firing biomass with coal – An example of Poland

  (Elsevier, 2026-05-21) Dzikuć, Maciej; Piwowar, Arkadiusz; Adamczyk, Janusz; Szufa, Szymon; Likozar, Blaž; Dzikuć, Maria; Faculty of Law and Economics, University of Zielona Góra. Dzikuć, Maciej and Adamczyk, Janusz and Dzikuć, Maria.; Faculty of Economics and Finance, Wroclaw University of Economics and Business. Piwowar, Arkadiusz.; Faculty of Process and Environmental Engineering, Lodz University of Technology. Szufa, Szymon.; Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry. Szufa, Szymon and Likozar, Blaž.

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  The article aims to indicate the potential of biomass as one of the elements of increasing the share of renewable energy sources while limiting the use of coal, which currently dominates the Polish energy sector. The use of biomass co-firing not only reduces coal consumption during electricity production but also reduces emissions, thus contributing to reducing energy production costs. The article characterizes the ecological and economic importance of developing the method of co-firing biomass with coal for electricity production and refers to previously published works on similar topics, which addressed other external conditions such as prices of energy raw materials and significantly lower CO2 emission fees. The research conducted in the article confirms the impact of using biomass during co-combustion with coal on reducing the environmental impact in one of the largest Polish coal-fired power plants, i.e. the Dolna Odra Power Plant.

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  Machine learning-based prediction of mercury and arsenic emissions under real operating conditions in pulverized coal boilers

  (Elsevier, 2026-07-07) Marczak-Grzesik, Marta; Regulski, Krzysztof; Junga, Robert; Bzowski, Krzysztof; Rauch, Łukasz; Górecki, Jerzy; Wierońska-Wiśniewska, Faustyna; Kogut, Krzysztof; Borovec, Karel; Szufa, Szymon; AGH University of Science and Technology, Faculty of Energy and Fuels. Marczak-Grzesik, Marta and Górecki, Jerzy and Wierońska-Wiśniewska, Faustyna and Kogut, Krzysztof.; Faculty of Space Technologies, AGH University of Krakow. Marczak-Grzesik, Marta.; AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science. Regulski, Krzysztof and Bzowski, Krzysztof and Rauch, Łukasz.; Department of Thermal Engineering and Industrial Facilities, Opole University of Technology. Junga, Robert.; VSB – Technical University of Ostrava. Borovec, Karel.; Lodz University of Technology, Faculty of Process and Environmental Engineering. Szufa, Szymon.

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  Mercury (Hg) and arsenic (As) emissions from coal-fired power plants remain a major environmental challenge due to their toxicity and complex partitioning behavior during combustion. This study presents a combined experimental and machine learning analysis of Hg and As emissions from two 225–240 MW pulverized coal boilers operating under variable load and 10% biomass co-firing conditions. A dataset comprising 73 operational variables and 497 measurement cases was used to develop regression and classification models. For mercury, multilayer perceptron (MLP) networks achieved good predictive performance, with the optimized 49–17-1 architecture reaching R2 = 0.92 and MAPE = 10.5% on the validation dataset. Feature reduction from 67 to 49 predictors improved generalization while preserving physical interpretability. Sensitivity analysis indicated that Hg emissions are influenced by combustion temperature, excess air ratio, NO2 concentration, and gas flow dynamics, highlighting the role of thermal and oxidative conditions. For arsenic, this work represents, to the best of our knowledge, one of the first applications of artificial neural networks for predicting flue gas As concentrations in pulverized coal boilers. The MLP 15–15-1 model achieved R2 = 0.971, with fuel arsenic content and particulate matter concentration identified as the most influential predictors. The developed approach enables both continuous emission prediction and threshold-based monitoring, supporting improved analysis of trace element behavior in coal combustion systems.

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  Życie Uczelni : biuletyn informacyjny Politechniki Łódzkiej nr 177 (2026)

  (Wydawnictwo Politechniki Łódzkiej, 2026) Politechnika Łódzka; Lodz University of Technology; Chojnacka, Ewa

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  Bibliografia publikacji pracowników Biblioteki Politechniki Łódzkiej z lat 1945-2025

  (Wydawnictwo Politechniki Łódzkiej, 2026) Sójkowska, Iwona (Oprac.)

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  Niniejsza publikacja zawiera wykaz dorobku piśmienniczego pracowników Biblioteki Politechniki Łódzkiej z lat 1945–2025. Jest przeglądem prac z zakresu bibliotekarstwa, bibliotekoznawstwa i informacji naukowej, a także innych dziedzin, pokazującym różnorodność warsztatu pracy, kompetencji i zainteresowań pracowników. Motywacją podjęcia prac nad rejestrem publikacji był Jubileusz 80-lecia Biblioteki Politechniki Łódzkiej, zaś założeniem autorki opracowania – zebranie i prezentacja danych w uporządkowanej i przejrzystej formie.(...)

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