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  • Machine learning approach to packaging compatibility testing in the new product development process
    • Norbert Piotrowski
    2024 Pełny tekst JOURNAL OF INTELLIGENT MANUFACTURING

    The paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing is mandatory inter alia for all aerosol packaging as any mechanical alterations of the packaging can cause the pressurized product to unseal and stop working properly. Moreover, aerosol products are classified as dangerous goods and any leaking of the product or propellent can be a serious hazard to the storage place, environment, and final consumer. Thus, basic compatibility observations of metal aerosol packaging (i.e. general corrosion, pitting corrosion, coating blistering or detinning) and different compatibility factors (e.g. formula ingredients, water contamination, pH, package material and coatings) were discussed. Artificial intelligence methods applied in the design process can reduce the lengthy testing time as well as developing costs and help benefit from the knowledge and experience of technologists stored in historical data in databases.

  • Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
    • Giao Van Nguyen
    • Prabhakar Sharma
    • Ümit Ağbulut
    • Huu Son Le
    • Thanh Hai Truong
    • Marek Dzida
    • Minh Ho Tran
    • Huu Cuong Le
    • Viet Dung Tran
    2024 Biofuels Bioproducts & Biorefining-Biofpr

    Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far-reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data-driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

  • Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
    • Rana Waqar Aslam
    • Hong Shu
    • Iram Naz
    • Abdul Quddoos
    • Andaleeb Yaseen
    • Khansa Gulshad
    • Saad Saud Alarifi
    2024 Pełny tekst Remote Sensing

    Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.

  • Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
    • Torkan Shafighfard
    • Farzin Kazemi
    • Neda Asgarkhani
    • Doo-Yeol Yoo
    2024 ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

    High-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a substantial amount of interest not only due to the potential it has for being used instead of ordinary concrete but also owing to the concerns associated with climate change, sustainability, reduction of CO2 emissions, and energy consumption. The characteristics and amounts of the ingredients used to produce HP-AAC influence its compressive strength. This study performs a comparative analysis based on machine learning (ML) algorithms to present an ensemble model capable of predicting the compressive strength of HP-AAC. This is in response to the development of sophisticated prediction approaches that seek to lower the cost of experimental tools and labor. An extensive framework including 538 experimental datasets with 18 input parameters are extracted. In addition, stacked ML (SM) models are developed to provide their best base estimator combination with the highest capability. The results show that stacked model (SM-5) with score of 14, and prediction accuracy of 98% following by the largest experiment-to-predicted ratio, provide the best estimations of compressive strength of HP-AAC, which has the lowest error values compare to other 18 ML models. Thereafter, a graphical user interface (GUI) is provided and validated by extra experimental tests for estimating the compressive strength, cost, and carbon emission of HP-AAC. Overall, the significance of the current study highlight the outstanding performance of developed stacked ML and GUI for predicting the compressive strength of HP-ACC, which contribute for the on-going research in this area.

  • Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
    • Farzin Kazemi
    • Neda Asgarkhani
    • Torkan Shafighfard
    • Robert Jankowski
    • Doo-Yeol Yoo
    2024 ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

    In recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.

  • Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024 Pełny tekst Scientific Reports

    Maximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit reliability, but the expense of conducting rudimentary EM-driven global optimization by means of popular bio-inspired algorithms is impractical. Similarly, nonlinear system characteristics pose challenges for surrogate-assisted methods. This paper introduces an innovative technique leveraging variable-fidelity EM simulations and response feature technology within a kriging-based machine-learning framework for cost-effective global parameter tuning of microwave passives. The efficiency of this approach stems from performing most operations at the low-fidelity simulation level and regularizing the objective function landscape through the response feature method. The primary prediction tool is a co-kriging surrogate, while a particle swarm optimizer, guided by predicted objective function improvements, handles the search process. Rigorous validation demonstrates the proposed framework's competitive efficacy in design quality and computational cost, typically requiring only sixty high-fidelity EM analyses, juxtaposed with various state-of-the-art benchmark methods. These benchmarks encompass nature-inspired algorithms, gradient search, and machine learning techniques directly interacting with the circuit's frequency characteristics.

  • Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    • Marek Wójcikowski
    • Bogdan Pankiewicz
    2024 Pełny tekst Engineering Science and Technology-An International Journal-JESTECH

    Air pollution remains a considerable contemporary challenge affecting life quality, the environment, and economic well-being. It encompasses an array of pollutants—gases, particulate matter, biological molecules—emanating from sources such as vehicle emissions, industrial activities, agriculture, and natural occurrences. Nitrogen dioxide (NO2), a harmful gas, is particularly abundant in densely populated urban areas. Given its detrimental impact on health and the environment, precise monitoring of NO2 levels is crucial for devising effective strategies to mitigate risks. However, precise measurement of NO2 presents challenges as it traditionally relies on expensive and heavy (therefore, stationary) equipment. This has led to the pursuit of more affordable alternatives, though their dependability is frequently questionable. This study introduces an innovative technique for precise calibration of low-cost NO2 sensors. Our methodology involves statistical preprocessing of sensor measurements to align their distributions with reference data. The core of the calibration model is an artificial neural network (ANN), trained to synchronize sensor and reference time series measurements. It incorporates environmental variables such as temperature, humidity, and atmospheric pressure, along with readings from redundant NO2 sensors for cross-referencing, and short time series of primary sensor NO2 measurements. This enables efficient learning of typical sensor changes over time in relation to these factors. Additionally, an interpolative kriging model serves as an auxiliary surrogate to enhance the correction process's reliability. Validation using an autonomous monitoring platform from Gdansk University of Technology, Poland, and public reference station data gathered over five months shows remarkable calibration accuracy, with a correlation coefficient close to 0.95 and RMSE of 2.4 µg/m3. These results position the corrected sensor as an attractive and cost-effective alternative to conventional NO2 measurement methods.

  • Macrocyclic derivatives of imidazole as chromoionophores for bismuth(III)/lead(II) pair
    • Błażej Galiński
    • Ewa Wagner-Wysiecka
    2024 Pełny tekst SENSORS AND ACTUATORS B-CHEMICAL

    18-membered diazomacrocycles with imidazole or 4-methylimidazole residue as a part of macrocycle were used as chromoionophores in bismuth(III) and lead(II) dual selective optodes for the first time. Cellulose triacetate membranes doped with macrocyclic chromoionophores are bismuth(III) and lead(II) selective with color change from orange/red to different shades of blue and violet, respectively. Results obtained for model and real samples of bismuth(III) and lead(II) showed that easily accessible and regenerable sensor materials can be used for spectrophotometric and colorimetric detection and determination of bismuth(III) and lead(II). The obtained LOD values for bismuth(III) are 1.63×10-7 M and 3.03×10-7 M with spectrophotometric and colorimetric detection, respectively, when using optode with imidazole residue. For sensing material with 4-methylimidazole in macroring the lowest detection limits were obtained for lead(II): 2.14×10-7 M and 3.99×10-7 M with spectrophotometric and digital color analysis detection mode, respectively.

  • Magnetic field mapping along a NV-rich nanodiamond-doped fiber
    • Adam Filipkowski
    • Mariusz Mrózek
    • Grzegorz Stępniewski
    • Mateusz Ficek
    • Dariusz Pysz
    • Wojciech Gawlik
    • Ryszard Buczyński
    • Adam M. Wojciechowski
    • Mariusz Klimczak
    2024 APPLIED PHYSICS LETTERS

    Integration of NV−-rich diamond with optical fibers enables guiding quantum information on the spin state of the NV− color center. Diamond-functionalized optical fiber sensors have been demonstrated with impressive sub-nanotesla magnetic field sensitivities over localized magnetic field sources, but their potential for distributed sensing remains unexplored. The volumetric incorporation of diamonds into the optical fiber core allows developing fibers sensitive to the magnetic field over their entire length. Theoretically, this makes distributed optical readout of small magnetic fields possible, but does not answer questions on the addressing of the spatial coordinate, i.e., the location of the field source, nor on the performance of a sensor where the NV− fluorescence is detected at one end, thereby integrating over color centers experiencing different field strength and microwave perturbation. Here, we demonstrate distributed magnetic field measurements using a step-index fiber with the optical core volumetrically functionalized with NV− diamonds. A microwave antenna on a translation stage is scanned along a 13 cm long section of a straight fiber. The NV− fluorescence is collected at the fiber's far end relative to the laser pump input end. Optically detected magnetic resonance spectra were recorded at the fiber output for every step of the antenna travel, revealing the magnetic field evolution along the fiber and indicating the magnetic field source location. The longitudinal distribution of the magnetic field along the fiber is detected with high accuracy. The simplicity of the demonstrated sensor would be useful for, e.g., magnetic-field mapping of photonics- and/or spintronics-based integrated circuits.

  • Magnetic hydrophobic deep eutectic solvents for orbital shaker-assisted dispersive liquid-liquid microextraction (MAGDES-OS-DLLME) - determination of nickel and copper in food and water samples by FAAS
    • Adil Elik
    • Hameed Haq
    • Grzegorz Boczkaj
    • Seçkin Fesliyan
    • Özlem Ablak
    • Nail Altunay
    2024 JOURNAL OF FOOD COMPOSITION AND ANALYSIS

    In this work, a cheap and widely applicable dispersive liquid-liquid microextraction (DLLME) method was developed for the extraction of Ni(II) and Cu(II) from water and food samples and analysis using flame atomic absorption spectrometry. DLLME was assisted by orbital shaker, while ferrofluid as an extractant was based on deep eutectic solvent (DES). This ferrofluid was made of hydrophobic DES (hDES), composed of lauric acid and menthol (molar ratio 1:2), and toner powder@aliquat 336 magnetic particles. The extraction procedure does not require any heating or centrifugation. The method limits of detection value were 0.15 µg L−1 and 0.03 µg L−1 for Ni(II) and Cu(II) respectively along with wide linearity range (0.4–250 µg L−1). The validation of the method was performed using certified reference materials (CRMs). The studies revealed excellent accuracy between results obtained by the developed method and expected values for all CRMs. The relative recoveries of Ni(II) and Cu(II) ions ranged from 92.8% to 98.6%. The developed method was further used for the determination of Ni(II) and Cu(II) in real water and food samples and provided quantitative recoveries.

  • Magnetic superhydrophobic melamine sponges for crude oil removal from water
    • Patrycja Makoś-Chełstowska
    • Edyta Słupek
    • Aleksandra Mielewczyk-Gryń
    • Tomasz Klimczuk
    2024 CHEMOSPHERE

    This paper proposes the preparation of a new sorbent material based on melamine sponges (MS) with superhydrophobic, superoleophilic, and magnetic properties. This study involved impregnating the surface of commercially available MS with eco-friendly deep eutectic solvents (DES) and Fe3O4 nanoparticles. The DES selection was based on the screening of 105 eutectic mixtures using COSMO-RS modeling. Other parameters affecting the efficiency and selectivity of oil removal from water were optimized using the Box-Bhenken model. Menthol:Thymol (1:1)@Fe3O4-MS exhibited the highest sorption capacity for real crude oils (101.7–127.3 g/g). This new sponge demonstrated paramagnetic behavior (31.06 emu/g), superhydrophobicity (151°), superoleophobicity (0°), low density (15.6 mg/cm3), high porosity (99 %), and excellent mechanical stability. Furthermore, it allows multiple regeneration processes without losing its sorption capacity. Based on these benefits, Menthol:Thymol (1:1)@Fe3O4-MS shows promise as an efficient, cost-effective, and eco-friendly substitute for the existing sorbents.

  • Magnetyczne i elektromagnetyczne uchwyty obróbkowe - konstrukcja i rozwój
    • Adam Barylski
    2024 Kwartalnik Naukowo-Techniczny "Obróbka Metalu"

    Przedstawiono współczesne rozwiązania konstrukcyjne obróbkowych uchwytów magnetycznych i elektromagnetycznych. Podano przykłady zastosowania w operacjach obróbki wiórowej i ściernej oraz w różnorodnych procesach spawania. Omówiono ważniejsze zalecenia technologiczne oraz ograniczenia praktyczne.

  • Management of ground tire rubber waste by incorporation into polyurethane-based composite foams
    • Aleksander Hejna
    • Paulina Kosmela
    • Adam Olszewski
    • Łukasz Zedler
    • Krzysztof Formela
    • Katarzyna Skórczewska
    • Adam Piasecki
    • Mariusz Marć
    • Roman Barczewski
    • Mateusz Barczewski
    2024 Pełny tekst ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH

    Rapid economic growth implicated the developing multiple industry sectors, including the automotive branch, increasing waste generation since recycling and utilization methods have not been established simultaneously. A very severe threat is the generation of enormous amounts of post-consumer tires considered burdensome waste, e.g., due to the substantial emissions of volatile organic compounds (VOCs). Therefore, it is essential to develop novel, environmentally friendly methods for their utilization, which would hinder their environmental impacts. One of the most promising approaches is shredding, resulting in the generation of ground tire rubber (GTR), which can be introduced into polymeric materials as filler. The presented work is related to the thermomechanical treatment of GTR in a twin-screw extruder with zinc borate, whose incorporation is aimed to enhance shear forces within the extruder barrel. Modified GTR was introduced into flexible polyurethane (PU) foams, and the impact of modification parameters on the cellular structure, static and dynamic mechanical performance, thermal stability, as well as thermal insulation, and acoustic properties were investigated. Emissions of VOCs from applied fillers and prepared composites were monitored and evaluated. Depending on the treatment parameters, beneficial changes in foams’ cellular structure were noted, which enhanced their thermal insulation performance, mechanical strength, and thermal stability. It was proven that the proposed method of GTR thermomechanical treatment assisted by zinc borate particles might benefit the performance of flexible PU foamed composites and hinder VOCs emissions, which could broaden the application range of GTR and provide novel ways for its efficient utilization

  • Managing and funding the innovative path: a close look at the SimLE scientific club at Gdańsk University of Technology, Poland
    • Wiktor Sieklicki
    • Maciej Zawadzki
    2024 Global Journal of Engineering Education

    This article presents a case study of the simply learn experience (SimLE) scientific club at Gdańsk University of Technology (Gdańsk Tech), Gdańsk, Poland, showcasing an effective model for blending theoretical knowledge with practical engineering applications. This student-led organisation aims to develop soft skills and handson experience through project work, participation in international contests and conferences. This study examines the funding mechanisms, educational impacts and management strategies of competitive achievements within SimLE. The results show that the level of financial support moderately affects the output of academic publications. However, this support is crucial for enhancing international visibility and facilitating participation in academic conferences. Also, it directly correlates with students’ engagement. Importantly, the research reveals that variations in scientific output are more closely associated with the composition of teams and management approaches rather than the extent of financial backing

  • Managing knowledge in a tourism crisis: case study from Poland
    • Ewa Stolarek-Muszyńska
    • Małgorzata Zięba
    • Ettore Bolisani
    • Enrico Scarso
    2024 Pełny tekst

    Purpose: This study deals with a tourism organisation from Poland, which experienced not only the COVID-19 pandemic, but also the close war situation in Ukraine which caused a significant decrease in tourist traffic and revenues. Since, based on the literature, knowledge management can be useful for crisis management, this study aims to explore the role and usefulness of KM during crisis situations in tourism. Methodology: Qualitative in-depth analysis was conducted by using data collected via semi-structured interview with the CEO of a local tourism organisation in Poland. The research output is presented in the form of a single case study with that organisation as the unit of analysis. Findings: The case highlighted that: a) a crisis may not be the most appropriate time for the implementation of KM from the scratch in an organisation; b) having some minimal KM experience can be essential for a more structured and complex KM approach; c) organisations may benefit from lessons learned during the crisis to get insights for developing KM. These findings suggest that practitioners and policymakers facilitate KM awareness among tourism organisations to enhance resilience in coping with future crises. Research limitations: This is a single case study and thus it cannot be easily generalised or provide a comprehensive overview of the whole sector. It is also a case study from a single country, affected by two serious crises which limits the applicability of the results to other countries. Practical implications: The study provides useful insights for practitioners in tourism organisations aspiring to improve internal processes of knowledge management and thus mitigating the future tourism crises. Originality/value: This paper contributes to the body of knowledge in terms of the role and relevance of KM during the tourism crisis. It provides food for thought for researchers investigating the knowledge and crisis management processes within the tourism industry.

  • Mangiferin: A comprehensive review on its extraction, purification and uses in food systems
    • Roberto Castro Munoz
    • René Cabezas
    • Maksymilian Plata Gryl
    2024 ADVANCES IN COLLOID AND INTERFACE SCIENCE

    With the target of fabricating healthier products, food manufacturing companies look for natural-based nutraceuticals that can potentially improve the physicochemical properties of food systems while being nutritive to the consumer and providing additional health benefits (biological activities). In this regard, Mangiferin joins all these requirements as a potential nutraceutical, which is typically contained in Mangifera indica products and its by-products. Unfortunately, knowing the complex chemical composition of Mango and its by-products, the extraction and purification of Mangiferin remains a challenge. Therefore, this comprehensive review revises the main strategies proposed by scientists for the extraction and purification of Mangiferin. Importantly, this review identifies that there is no report reviewing and criticizing the literature in this field so far. Our attention has been targeted on the timely findings on the primary extraction techniques and the relevant insights into isolation and purification. Our discussion has emphasized the advantages and limitations of the proposed strategies, including solvents, extracting conditions and key interactions with the target xanthone. Additionally, we report the current research gaps in the field after analyzing the literature, as well as some examples of functional food products containing Mangiferin.

  • Marine polymers in tissue bioprinting: Current achievements and challenges
    • Adrianna Banach-Kopeć
    • Szymon Mania
    • Robert Tylingo
    2024 Pełny tekst REVIEWS ON ADVANCED MATERIALS SCIENCE

    Bioprinting has a critical role in tissue engineering, allowing the creation of sophisticated cellular scaffolds with high resolution, shape fidelity, and cell viability. Achieving these parameters remains a challenge, necessitating bioinks that are biocompatible, printable, and biodegradable. This review highlights the potential of marine-derived polymers and crosslinking techniques including mammalian collagen and gelatin along with their marine equivalents. While denaturation temperatures vary based on origin, warm-water fish collagen and gelatin emerge as promising solutions. Building on the applications of mammalian collagen and gelatin, this study investigates their marine counterparts. Diverse research groups present different perspectives on printability and cell survival. Despite advances, current scaffolds are limited in size and layers, making applications such as extensive skin burn treatment or tissue regeneration difficult. The authors argue for the development of bioprinting, which includes spherical and adaptive printing. In adaptive printing, layers differentiate and propagate sequentially to overcome the challenges of multilayer printing and provide optimal conditions for the growth of deeply embedded cells. Moving the boundaries of bioprinting, future prospects include transformative applications in regenerative medicine.

  • Maritime traffic situation awareness analysis via high-fidelity ship imaging trajectory
    • Xinqiang Chen
    • Jinbiao Zheng
    • Chaofeng Li
    • Bing Wu
    • Huafeng Wu
    • Jakub Montewka
    2024 MULTIMEDIA TOOLS AND APPLICATIONS

    Situation awareness provides crucial yet instant information to maritime traffic participants, and significant attentions are paid to implement traffic situation awareness task via various maritime data source (e.g., automatic identification system, maritime surveillance video, radar, etc.). The study aims to analyze traffic situation with the support of ship imaging trajectory. First, we employ the dark channel prior model to remove fog in maritime videos to obtain high-resolution ship images (i.e., fog-free maritime images). Second, we track ships in each maritime image with the scale adaptive kernel correlation filter (SAMF), and thus obtain raw ship imaging trajectories. Third, we cleanse abnormal ship trajectory samples via curve-fitting and down sampling method, and thus further maritime traffic situation analysis is implemented. We analyze maritime traffic situation in three typical videos (i.e., three typical maritime traffic scenarios), and experimental results suggested that the proposed framework can extract high-resolution ship imaging trajectory for fulfilling the task of accurate maritime traffic situation awareness.

  • MARS - BAZA. warsztaty pozaziemskiej architektury ekstremalnej. Warsztaty w ramach Bałtyckiego Festiwalu Nauki
    • Aleksandra Karpińska
    • Agnieszka Kurkowska
    • Marta Koperska-Kośmicka
    • Marcin Kulesza
    2024

    Jak przetrwać w różnych warunkach? Czego potrzebujemy, by przeżyć, a czego, by żyć wygodnie? Poszukamy odpowiedzi na te pytania, by stanąć przed nie lada misją: wspólnie podejmiemy się największego wyzwania przyszłości - zbudujemy bazę na Marsie! Budowa schronienia, bazy, domu - troska o zaspokojenie podstawowych potrzeb towarzyszy nam od zawsze, a budowanie jest jednym z pierwszych trwałych działań ludzi, pomagającym spełnić nasze potrzeby bytowe. Z innymi wyzwaniami stykamy się jednak, kiedy planujemy budować dom, a innymi, kiedy spotykamy się z warunkami ekstremalnymi, kiedy standardowe rozwiązania nie mają zastosowania. Lekcja myślenia o projektowaniu w warunkach ekstremalnych to wstęp do zadania praktycznego w nurcie dizajnu spekulatywnego: uczestnicy będą projektować i budować w skali 1:1 model własnej bazy na Marsie.

  • Maximizing Bio-Hydrogen and Energy Yields Obtained in a Self-Fermented Anaerobic Bioreactor by Screening of Different Sewage Sludge Pretreatment Methods
    • Alaa A. El-kebeer
    • Usama F. Mahmoud
    • Sayed Ismail
    • Abu Abbas E. Jalal
    • Przemysław Kowal
    • Hussein Al-Hazmi
    • Gamal K. Hassan
    2024 Pełny tekst Processes

    Egypt faces significant challenges in managing its sewage sludge generated in large quantities from wastewater treatment plants. This study investigates the feasibility of utilizing sewage sludge as a renewable resource for hydrogen production through anaerobic digestion at the 100 L bioreactor level. Hydrogen is considered a promising alternative energy source due to its high energy content and environmental benefits. To optimize the microbial degradation process and maximize hydrogen production from sewage sludge, a specialized pretreatment is necessary. Various pretreatment methods have been applied to the sewage sludge, individually and in combination, to study the bio-hydrogen production from sewage sludge. The four methods of treatment were studied in batch assays as a pilot scale. Thermal pretreatment of sewage sludge significantly increases bio-hydrogen production yield compared to other sewage sludge pretreatment methods, producing the highest H2 yield (6.48 LH2/g VS). In general, the hydrogen yield of any type of pretreated inoculum was significantly higher than the untreated inoculum. At the same time, alkaline pretreatment improved the hydrogen yield (1.04 LH2/g VS) more than acid pretreatment (0.74 LH2/g VS), while the hydrogen yield for the combination of pretreatments (shock alkali pretreatment) was higher than both (1.73 LH2/g VS), On the other hand, untreated sewage sludge (control) had almost no hydrogen yield (0.03 LH2/g VS). The self-fermented anaerobic bioreactor improved sewage sludge utilization, increased bioenergy yields, and seems to be promising for treating complex wastes at this scale.