398 research outputs found
Hydrogen production from plastic waste: A comprehensive simulation and machine learning study
Gasification, a highly efficient method, is under extensive investigation due to its potential to convert biomass and plastic waste into eco-friendly energy sources and valuable fuels. Nevertheless, there exists a gap in comprehension regarding the integrated thermochemical process of polystyrene (PS) and polypropylene (PP) and its capability to produce hydrogen (H2) fuel. In this study a comprehensive process simulation using a quasi-equilibrium approach based on minimizing Gibbs free energy has been introduced. To enhance H2 content, a water-gas shift (WGS) reactor and a pressure swing adsorption (PSA) unit were integrated for effective H2 separation, increasing H2 production to 27.81 kg/h. To investigate the operating conditions on the process the effects of three key variables in a gasification reactor namely gasification temperature, feedstock flow rate and gasification pressure have been explored using sensitivity analysis. Furthermore, several machine learning models have been utilized to discover and optimize maximum capacity of the process for H2 production. The sensitivity analysis reveals that elevating the gasification temperature from 500 °C to 1200 °C results in higher production of H2 up to 23 % and carbon monoxide (CO). However, generating H2 above 900 °C does not lead to a significant upturn in process capacity. Conversely, an increase in pressure within the gasification reactor is shown to decrease the system capacity for generating both H2 and CO. Moreover, increasing the mass flow rate of the gasifying agent to 250 kg/h in the gasification reactor has shown to be merely productive in process capacity for H2 generation, almost a 5 % increase. Regarding pressure, the hydrogen yield decreases from 22.64 % to 17.4 % with an increase in pressure from 1 to 10 bar. It has been also revealed that gasification temperature has more predominant effect on Cold gas efficiency (CGE) compared to gasification pressure and Highest CGE Has been shown by PP at 1200 °C. Among the various machine learning models, Random Forest (RF) model demonstrates robust performance, achieving R2 values exceeding 0.99
Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality
As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel.
In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach.
Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties.
Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel
Systemic Circular Economy Solutions for Fiber Reinforced Composites
This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials
Synthesis of carbon nanotubes supported iron catalysts for light olefins via Fischer-Tropsch synthesis
Light olefins including ethylene, propylene and butylene are the basics of many chemical products. As the demand for light olefins is dramatically increased and oil resources are limited, it becomes desirable to produce light olefins from other resources such as syngas. Syngas could be obtained from alternative feedstocks such as methane, coal, biomass, and plastic wastes. Fischer-Tropsch (FT) synthesis involves conversion of syngas to hydrocarbons. FT products at high temperatures are mainly gasoline and light olefins. In FT catalytic reaction, iron is preferred due to its low cost, high selectivity towards olefins and flexibility in terms of use for different ratio of H2 to CO in syngas feed. In this study, catalytic performance of iron catalyst was evaluated using molybdenum and potassium as promoters and carbon nanotubes (CNTs) as support. The study plan for this research was divided into four sub-objectives or phases.
In the first phase, catalytic chemical vapor deposition (CCVD) method was applied to synthesize CNTs using Fe/CaCO3 and acetylene as catalyst and hydrocarbon source, respectively. Applying response surface methodology, the optimum operating conditions were determined in CVD reactor for maximal yield and purity of CNTs. The effects of reaction time (30–60 min), reaction temperature (700–800 °C), and loading of the catalyst (10–30 wt% Fe) were investigated. 20Fe/CNTs-synthesized, 20Fe/CNTs-commercial, and 20Fe/Al2O3 were analyzed in terms of physio-chemical properties and FTS catalytic performance. The catalytic performance of Fe-based catalysts was investigated using a fixed-bed reactor at 280 °C under 2.0 MPa. 20Fe/CNTs-synthesized exhibited a lower rate of water-gas-shift (WGS) reaction compared with 20Fe/CNTs-commercial, with C2-C4 selectivity of 23.6% which is slightly less than that of its commercial counterpart. After 120 h time-on-stream under steady state condition, the higher activity was maintained by the 20Fe/CNTs-synthesized catalyst compared to the 20Fe/CNTs-Commercial and 20Fe/Al2O3 catalysts.
Electronic structural promoters such as K and Mo improve olefins’ selectivity and catalytic activity. Hence, in the second phase, CNTs synthesized by CCVD were used as support to obtain K- and/or Mo-promoted Fe/CNTs catalysts for light olefins’ production in FTS. A two-level full factorial design was applied for K- and/or Mo-promoted Fe/CNTs catalyst to investigate the effects of synthesis conditions including Mo/K mass ratio, ultrasonic time, and iron loading on light olefins’ yield. CO chemisorption and TEM revealed that molybdenum plays a significant role in metal dispersion, leaving structural defects on CNTs support.
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Additionally, H2-TPR confirmed that K as promoter facilitates reducibility of Fe/CNTs catalysts, which promoted CO conversion in FTS. Compared with the un-promoted Fe/CNTs catalysts, addition of molybdenum as a promoter increased light olefins' selectivity by 33.4%, while potassium led to an increase in CO conversion by 96.3%. The optimum formulation (0.5K5Mo10Fe/CNTs) obtained the olefins’ yield of 35.5%.
In the third phase the kinetic study of FTS was performed over the optimum bimetallic promoted catalyst (0.5K5Mo10Fe/CNTs) in a fixed-bed reactor by collecting experimental data over a wide range of industrially relevant reaction conditions (P = 0.68–4.13 MPa, T = 270-290 °C, H2/CO = 1, GHSV = 2000 h-1). Based on the adsorption of carbon monoxide and hydrogen, twenty-two possible mechanisms for monomer formation during Fischer-Tropsch synthesis were proposed in accordance with the Langmuir-Hinshelwood-Hougen-Watson (LHHW) and Eley-Rideal (ER) adsorption theories. Kinetic parameters such as activation energy, adsorption enthalpies of H2 and CO were estimated to be 65.0, -13.0, and -54.0 kJ/mol, respectively. Based on the developed kinetic model, the effects of reaction temperature and pressure were assessed on FTS product distribution. In addition, the Anderson-Schulz-Flory model was applied to further assess the reliability of the best fit mechanistic model for a wide range of hydrocarbon products.
In the fourth phase, techno-economic analysis (TEA) and life cycle assessment (LCA) of light olefin production in Fischer-Tropsch synthesis reaction were investigated via different scenarios. Data from a lab-scale experiment using the optimum bimetallic promoted catalyst (0.5K5Mo10Fe/CNTs) were used to simulate a plant to produce 1 kg of ethylene/h. The economic feasibility of light olefins production was estimated based on a comprehensive cash flow analysis. The net rate of return (NRR) was calculated to 5.6%, 7.4%, and 18.2% for the base scenario (scenario 1), scenario 2 with wastewater treatment, and scenario 3 with wastewater treatment-separation unit, respectively, which means the project is profitable from an economic perspective. The GHG emissions performance was measured as 77.5 g CO2-eq per MJ ethylene confirming the significant GHG emissions decrease compared to petroleum-based fuels production (3686 g CO2-eq per MJ ethylene)
Analyzing temperature distribution in pyrolysis systems using an atomic model
Pyrolysis is a complex energy conversion reaction due to the multiple stages of the process, the interaction of kinetics, mass and heat transfer and thermodynamics. The feedstock, temperature, heating rate, residence time, and reactor design are only a few factors that might impact the final product during the pyrolysis process. This study focuses on the temperature analysis of pyrolysis with sheep manure as feedstock, which includes reactor, pipes and condenser. The examination of the temperature distribution within a pyrolysis system can contribute to the preservation of product quality, the maintenance of heat balance, and the enhancement of energy efficiency. Based on the analysis, the degradation temperature of sheep manure is between 210–500 ℃. Consequently, it is crucial to control the reactor temperature at a desirable temperature that aligns with the degradation temperature of sheep manure. To ensure optimal condensation and maximize bio-oil yield, it is also necessary to control the condenser temperature. This study aims to determine the characteristics of temperature changes in pyrolysis systems using atomic models. The atomic model was built in OpenModelica using the Modelica language. The atomic model was validated with experiment, and it was found that there was a significant difference in reactor temperature. Complex processes occur in the reactor where pyrolysis occurs and various factors can impact the temperature of the reaction. The temperature in the multistage condenser gradually decreases by 1–3 ℃. In the principle of condensation, this temperature drop is considered less than optimal because the cooling fluid in the pyrolysis condensation system is air coolant, which is entirely reliant on ambient temperature. The accuracy of the atomic model is evaluated using error analysis and the mean absolute percentage error (MAPE). A value of 13.6% was calculated using the MAPE. The atomic model can be applied because this value is still within the tolerance range
Anaerobic Digestion Process Modeling Under Uncertainty: A Narrative Review
Growing concern about global climate change has led to considerable interest in investigating renewable energy sources such as the biological conversion of biomass to methane in an anaerobic environment. Through a series of complicated biochemical interactions, it uses various bacterial species to degrade biodegradable material in the feedstock. Due to the complex and interacting biochemical processes, anaerobic digestion has nonlinear dynamics. Anaerobic digestion is highly at risk of instabilities and uncertainties because of its dynamic and nonlinear behavior, uncertain feedstock quality, and sensitivity to the process’s environmental conditions. Therefore, effectively operating a biogas production unit necessitates a thorough understanding of the system’s uncertainties. The present study aims to identify and assess the sources and methods of coping with the uncertainties in anaerobic digestion processes through a narrative review. Moreover, the knowledge gap is also investigated to reveal the challenges and opportunities to have a robust model. The results indicate that the unpredictability of model parameters and input variables were the most significant source of uncertainty, and the Monte Carlo technique, confident interval, and interval observers, as well as sensitivity analysis were the most frequently used tools to cope with these uncertainties
Machine learning and computational chemistry to improve biochar fertilizers : a review
Traditional fertilizers are highly inefficient, with a major loss of nutrients and associated pollution. Alternatively, biochar loaded with phosphorous is a sustainable fertilizer that improves soil structure, stores carbon in soils, and provides plant nutrients in the long run, yet most biochars are not optimal because mechanisms ruling biochar properties are poorly known. This issue can be solved by recent developments in machine learning and computational chemistry. Here we review phosphorus-loaded biochar with emphasis on computational chemistry, machine learning, organic acids, drawbacks of classical fertilizers, biochar production, phosphorus loading, and mechanisms of phosphorous release. Modeling techniques allow for deciphering the influence of individual variables on biochar, employing various supervised learning models tailored to different biochar types. Computational chemistry provides knowledge on factors that control phosphorus binding, e.g., the type of phosphorus compound, soil constituents, mineral surfaces, binding motifs, water, solution pH, and redox potential. Phosphorus release from biochar is controlled by coexisting anions, pH, adsorbent dosage, initial phosphorus concentration, and temperature. Pyrolysis temperatures below 600 °C enhance functional group retention, while temperatures below 450 °C increase plant-available phosphorus. Lower pH values promote phosphorus release, while higher pH values hinder it. Physical modifications, such as increasing surface area and pore volume, can maximize the adsorption capacity of phosphorus-loaded biochar. Furthermore, the type of organic acid affects phosphorus release, with low molecular weight organic acids being advantageous for soil utilization. Lastly, biochar-based fertilizers release nutrients 2–4 times slower than conventional fertilizers
PhD students´day FMST 2023
The authors gave oral presentations of their work online as part of a Doctoral Students’ Day held on 15 June 2023, and they reflect the challenging work done by the students and their supervisors in the fields of metallurgy, materials engineering and management. There are 82 contributions in total, covering a range of areas – metallurgical technology, thermal engineering and fuels in industry, chemical metallurgy, nanotechnology, materials science and engineering, and industrial systems management. This represents a cross-section of the diverse topics investigated by doctoral students at the faculty, and it will provide a guide for Master’s graduates in these or similar disciplines who are interested in pursuing their scientific careers further, whether they are from the faculty here in Ostrava or engineering faculties elsewhere in the Czech Republic. The quality of the contributions varies: some are of average quality, but many reach a standard comparable with research articles published in established journals focusing on disciplines of materials technology. The diversity of topics, and in some cases the excellence of the contributions, with logical structure and clearly formulated conclusions, reflect the high standard of the doctoral programme at the faculty.Ostrav
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