Techno-economic analysis and method development applied to an aerobic gas fermentation and supercritical water gasification process

The chemical and fuels industry's reliance on fossil-based feedstocks necessitates a shift to low-emission renewables like agricultural residues, municipal solid wastes, and industrial off-gases. Gas fermentation employs microbes to convert gaseous carbon-rich streams into renewable chemicals. The current state of commercial gas fermentation relies on anaerobic bacteria. However, aerobic bacteria offer the potential to target a broader range of products. Despite this, an inherent disadvantage of aerobic gas fermentation is its poor thermodynamic efficiency. Integrating aerobic gas fermentation with Supercritical Water Gasification (SCWG) addresses this challenge, creating a promising biochemical production platform. The integration utilises the low-temperature fermentation heat to pre-heat biomass for SCWG and reclaims the energy from depressurising the SCWG effluent using a turbo-expander. In addition, a key benefit of SCWG is its ability to exploit wet, low-value wastes. Such feedstocks are abundantly available and have limited resource competition as they are uneconomical to exploit via conventional gasification. Despite these benefits, the economic feasibility needs verifying which includes the selection of optimal feedstocks, feasible production capacities, and geographic considerations to identify promising biorefinery scenarios. Essential for assessing emerging technologies, Techno-Economic Analyses (TEAs) were conducted to rigorously model and assess two case studies for the proposed integrated technology. The first considered commodity chemicals as direct products from fermentation (co-produced isopropanol and acetone), achieving a cumulative Net Present Value (NPV) of $42 million. Results compared favourably to anaerobic fermentation, with a minimum fuel selling price of$2.87/GGE. The second case study considered hybrid processing, integrating bio- and chemo-catalytic upgrading to produce 1,3-butadiene. This process showed profitability, achieving an MSP of $1367/tn, a$2.8M NPV, and a 19% probability of positive NPV. Part of the success of these two case studies was due the use of low-cost black liquor as the feedstock. SCWG allows for the successful exploitation of this wet feedstock. As such, a final study was undertaken to identify promising biorefinery scenarios for hydrogen production via SCWG, considering different feedstock-capacity-location combinations. The Levelised Cost of Hydrogen (LCOH) ranged from 3.81 to 18.72 $/kgH2 across the considered scenarios. At capacities >50 m3/h, the LCOH’s (2.76–4.21$/kgH2 for China, 3.41–5.07 $/kgH2 for Brazil, 4.31–6.62$/kgH2 for the UK) were competitive with MW-scale electrolysis costs (3.10–6.70 $/kgH2 for China, 3.70–5.90$/kgH2 for Brazil, and 4.81–6.31 $/kgH2 for the UK). The range of results highlights the significance of feedstock-capacity-location considerations during technology evaluation. In evaluating the economic feasibility of bio-derived chemicals and fuels, it's crucial to conduct Life Cycle Assessment (LCA) to quantify environmental impact. This facilitates a comparison of the trade-offs between a process’ economic and environmental performance. For both commodity chemical case studies net negative emissions were achieved due to biogenic carbon sequestration. Isopropanol and acetone exhibited GHG emissions of -2.10 and -2.21 kgCO2eq/kg compared to conventional production of 2.07 and 2.43 kgCO2eq/kg. For 1,3-butadiene production emissions were -3.23 kgCO2eq/kg, contrasting with the conventional 1.2 kgCO2eq/kg. Hydrogen production from the final case study also demonstrated low process emissions, averaging 0.46 kgCO2eq/GJH2 (China and Brazil), and 0.37 kgCO2eq/GJH2 (UK), compared to 8 kgCO2eq/GJH2 for steam methane reforming with carbon capture and storage (excluding natural gas leakage). These favourable emissions across all studies highlight the benefits of exploiting low-value, low-emission feedstocks. As part of a TEA product prices for 20-25 years into the future are required to assess potential profitability. A Machine Learning (ML) method for projecting future commodity prices was developed to allow for unbiased price selection procedures to input into TEAs. Initially, a Radial Basis Function Neural Network (RBFNN) was trained using 10 historic prices, optimising weights and centre points. The model was run recursively, with predicted prices becoming inputs. Stochastic uncertainty was incorporated using a ±30/20$35-$95M to$45-$80M (isopropanol and acetone) and from -$45M-$65M to -$35M-$80M (1,3-butadiene), highlighting the importance of price selection procedures on TEA outcomes. Conducting TEAs is time consuming and requires expert knowledge, hindering widespread application. To facilitate quick biorefinery scenario evaluations a ML method was developed. This was created for the TEA of hydrogen production via SCWG. An ML surrogate model was developed to predict the LCOH based on different feedstock-capacity-location combinations. The training data included 40 biomass compositions, five processing capacities (ranging from 10 to 200 m3/hr), and three geographical locations (China, Brazil, UK). Three ML algorithms were compared for the ML surrogate model: Random Forests, Support Vector Regression, and an ensemble of Artificial Neural Networks (ANNs). The ANN ensemble was the most accurate during cross-validation and achieved an accuracy of Mean Absolute Percentage Error: 0.99 on the test set. The final model was published for users to evaluate their own feedstocks. Overall, the model enables the identification of promising biorefinery scenarios for valorisation to maximise the economic potential of the technology. There are two key contributing areas of this thesis, firstly, the rigorous techno-economic and environmental assessment of the technology and secondly, the development of TEA methods using ML to aid these evaluations. The techno-economic and environmental assessment demonstrates the economic and environmental viability of the proposed technology platform compared to both conventional and alternative renewable production routes. The development of TEA methods used ML to create an unbiased methodology to select product price and price distributions in TEAs and to produce a TEA surrogate model for early-stage screening of feedstock scenarios for SCWG. The methods developed demonstrate the potential of ML to enhance TEA practices

Techno-economic analysis and method development applied to an aerobic gas fermentation and supercritical water gasification process

The chemical and fuels industry's reliance on fossil-based feedstocks necessitates a shift to low-emission renewables like agricultural residues, municipal solid wastes, and industrial off-gases. Gas fermentation employs microbes to convert gaseous carbon-rich streams into renewable chemicals. The current state of commercial gas fermentation relies on anaerobic bacteria. However, aerobic bacteria offer the potential to target a broader range of products. Despite this, an inherent disadvantage of aerobic gas fermentation is its poor thermodynamic efficiency. Integrating aerobic gas fermentation with Supercritical Water Gasification (SCWG) addresses this challenge, creating a promising biochemical production platform. The integration utilises the low-temperature fermentation heat to pre-heat biomass for SCWG and reclaims the energy from depressurising the SCWG effluent using a turbo-expander. In addition, a key benefit of SCWG is its ability to exploit wet, low-value wastes. Such feedstocks are abundantly available and have limited resource competition as they are uneconomical to exploit via conventional gasification. Despite these benefits, the economic feasibility needs verifying which includes the selection of optimal feedstocks, feasible production capacities, and geographic considerations to identify promising biorefinery scenarios. Essential for assessing emerging technologies, Techno-Economic Analyses (TEAs) were conducted to rigorously model and assess two case studies for the proposed integrated technology. The first considered commodity chemicals as direct products from fermentation (co-produced isopropanol and acetone), achieving a cumulative Net Present Value (NPV) of $42 million. Results compared favourably to anaerobic fermentation, with a minimum fuel selling price of$2.87/GGE. The second case study considered hybrid processing, integrating bio- and chemo-catalytic upgrading to produce 1,3-butadiene. This process showed profitability, achieving an MSP of $1367/tn, a$2.8M NPV, and a 19% probability of positive NPV. Part of the success of these two case studies was due the use of low-cost black liquor as the feedstock. SCWG allows for the successful exploitation of this wet feedstock. As such, a final study was undertaken to identify promising biorefinery scenarios for hydrogen production via SCWG, considering different feedstock-capacity-location combinations. The Levelised Cost of Hydrogen (LCOH) ranged from 3.81 to 18.72 $/kgH2 across the considered scenarios. At capacities >50 m3/h, the LCOH’s (2.76–4.21$/kgH2 for China, 3.41–5.07 $/kgH2 for Brazil, 4.31–6.62$/kgH2 for the UK) were competitive with MW-scale electrolysis costs (3.10–6.70 $/kgH2 for China, 3.70–5.90$/kgH2 for Brazil, and 4.81–6.31 $/kgH2 for the UK). The range of results highlights the significance of feedstock-capacity-location considerations during technology evaluation. In evaluating the economic feasibility of bio-derived chemicals and fuels, it's crucial to conduct Life Cycle Assessment (LCA) to quantify environmental impact. This facilitates a comparison of the trade-offs between a process’ economic and environmental performance. For both commodity chemical case studies net negative emissions were achieved due to biogenic carbon sequestration. Isopropanol and acetone exhibited GHG emissions of -2.10 and -2.21 kgCO2eq/kg compared to conventional production of 2.07 and 2.43 kgCO2eq/kg. For 1,3-butadiene production emissions were -3.23 kgCO2eq/kg, contrasting with the conventional 1.2 kgCO2eq/kg. Hydrogen production from the final case study also demonstrated low process emissions, averaging 0.46 kgCO2eq/GJH2 (China and Brazil), and 0.37 kgCO2eq/GJH2 (UK), compared to 8 kgCO2eq/GJH2 for steam methane reforming with carbon capture and storage (excluding natural gas leakage). These favourable emissions across all studies highlight the benefits of exploiting low-value, low-emission feedstocks. As part of a TEA product prices for 20-25 years into the future are required to assess potential profitability. A Machine Learning (ML) method for projecting future commodity prices was developed to allow for unbiased price selection procedures to input into TEAs. Initially, a Radial Basis Function Neural Network (RBFNN) was trained using 10 historic prices, optimising weights and centre points. The model was run recursively, with predicted prices becoming inputs. Stochastic uncertainty was incorporated using a ±30/20$35-$95M to$45-$80M (isopropanol and acetone) and from -$45M-$65M to -$35M-$80M (1,3-butadiene), highlighting the importance of price selection procedures on TEA outcomes. Conducting TEAs is time consuming and requires expert knowledge, hindering widespread application. To facilitate quick biorefinery scenario evaluations a ML method was developed. This was created for the TEA of hydrogen production via SCWG. An ML surrogate model was developed to predict the LCOH based on different feedstock-capacity-location combinations. The training data included 40 biomass compositions, five processing capacities (ranging from 10 to 200 m3/hr), and three geographical locations (China, Brazil, UK). Three ML algorithms were compared for the ML surrogate model: Random Forests, Support Vector Regression, and an ensemble of Artificial Neural Networks (ANNs). The ANN ensemble was the most accurate during cross-validation and achieved an accuracy of Mean Absolute Percentage Error: 0.99 on the test set. The final model was published for users to evaluate their own feedstocks. Overall, the model enables the identification of promising biorefinery scenarios for valorisation to maximise the economic potential of the technology. There are two key contributing areas of this thesis, firstly, the rigorous techno-economic and environmental assessment of the technology and secondly, the development of TEA methods using ML to aid these evaluations. The techno-economic and environmental assessment demonstrates the economic and environmental viability of the proposed technology platform compared to both conventional and alternative renewable production routes. The development of TEA methods used ML to create an unbiased methodology to select product price and price distributions in TEAs and to produce a TEA surrogate model for early-stage screening of feedstock scenarios for SCWG. The methods developed demonstrate the potential of ML to enhance TEA practices

Modeling the effect of blending multiple components on gasoline properties

Global CO2 emissions reached a new historical maximum in 2018 and transportation sector contributed to one fourth of those emissions. Road transport industry has started moving towards more sustainable solutions, however, market penetration for electric vehicles (EV) is still too slow while regulation for biofuels has become stricter due to the risk of inflated food prices and skepticism regarding their sustainability. In spite of this, Europe has ambitious targets for the next 30 years and impending strict policies resulting from these goals will definitely increase the pressure on the oil sector to move towards cleaner practices and products. Although the use of biodiesel is quite extended and bioethanol is already used as a gasoline component, there are no alternative drop-in fuels compatible with spark ignition engines in the market yet. Alternative feedstock is widely available but its characteristics differ from those of crude oil, and lack of homogeneity and substantially lower availability complicate its integration in conventional refining processes. This work explores the possibility of implementing Machine Learning to develop predictive models for auto-ignition properties and to gain a better understanding of the blending behavior of the different molecules that conform commercial gasoline. Additionally, the methodology developed in this study aims to contribute to new characterization methods for conventional and renewable gasoline streams in a simpler, faster and more inexpensive way. To build the models included in this thesis, a palette with seven different compounds was chosen: n-heptane, iso-octane, 1-hexene, cyclopentane, toluene, ethanol and ETBE. A data set containing 243 different combinations of the species in the palette was collected from literature, together with their experimentally measured RON and/or MON. Linear Regression based on Ordinary Least Squares was used as the baseline to compare the performance of more complex algorithms, namely Nearest Neighbors, Support Vector Machines, Decision Trees and Random Forest. The best predictions were obtained with a Support Vector Regression algorithm using a non-linear kernel, able to reproduce synergistic and antagonistic interaction between the seven molecules in the samples

Multi-scale modelling and optimisation of sustainable chemical processes

This dissertation explores the process modelling and optimisation of chemical processes under sustainability criteria. Resting on process systems engineering techniques combined with life cycle assessment (LCA), we present implementation strategies to improve flowsheet performance and reduce environmental impacts from early design stages. We first address the relevance of sustainability assessments in the sector and present process and environmental modelling techniques available. Under the observation that chemical processes are subject to market, technical, and environmental fluctuations, we next present an approach to account for these uncertainties. Process optimisation is then tackled by combining surrogate modelling, objective-reduction, and multi-criteria decision analysis tools. The framework proved the enhancement of the assessments by reducing the use of computational resources and allowing the ranking of optimal alternatives based on the concept of efficiency. We finally introduce a scheme to assess sustainable performance at a multi-scale level, from catalysis development to planet implications. This approach aims to provide insights about the role of catalysis and establish priorities for process development, while also introducing absolute sustainability metrics via the concept of ‘Planetary boundaries’. Ultimately, this allows a clear view of the impact that a process incurs in the current and future status of the Earth. The capabilities of the methods developed are tested in relevant applications that address challenges in the sector to attain sustainable performance. We present how concepts like circular economy, waste valorisation, and renewable raw materials can certainly bring benefits to the industry compared to their fossil-based alternatives. However, we also show that the development of new processes and technologies is very likely to shift environmental impacts from one category to another, concluding that cross-sectorial cooperation will become essential to meet sustainability targets, such as those determined by the Sustainable Development Goals.Open Acces

Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.Zlepšení průmyslových procesů, Model založený na datech, Optimalizace procesu, Strojové učení, Průmyslové systémy, Energeticky náročná průmyslová odvětví, Umělá inteligence.

Book of abstracts of the 10th International Chemical and Biological Engineering Conference: CHEMPOR 2008

This book contains the extended abstracts presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, over 3 days, from the 4th to the 6th of September, 2008. Previous editions took place in Lisboa (1975, 1889, 1998), Braga (1978), Póvoa de Varzim (1981), Coimbra (1985, 2005), Porto (1993), and Aveiro (2001). The conference was jointly organized by the University of Minho, “Ordem dos Engenheiros”, and the IBB - Institute for Biotechnology and Bioengineering with the usual support of the “Sociedade Portuguesa de Química” and, by the first time, of the “Sociedade Portuguesa de Biotecnologia”. Thirty years elapsed since CHEMPOR was held at the University of Minho, organized by T.R. Bott, D. Allen, A. Bridgwater, J.J.B. Romero, L.J.S. Soares and J.D.R.S. Pinheiro. We are fortunate to have Profs. Bott, Soares and Pinheiro in the Honor Committee of this 10th edition, under the high Patronage of his Excellency the President of the Portuguese Republic, Prof. Aníbal Cavaco Silva. The opening ceremony will confer Prof. Bott with a “Long Term Achievement” award acknowledging the important contribution Prof. Bott brought along more than 30 years to the development of the Chemical Engineering science, to the launch of CHEMPOR series and specially to the University of Minho. Prof. Bott’s inaugural lecture will address the importance of effective energy management in processing operations, particularly in the effectiveness of heat recovery and the associated reduction in greenhouse gas emission from combustion processes. The CHEMPOR series traditionally brings together both young and established researchers and end users to discuss recent developments in different areas of Chemical Engineering. The scope of this edition is broadening out by including the Biological Engineering research. One of the major core areas of the conference program is life quality, due to the importance that Chemical and Biological Engineering plays in this area. “Integration of Life Sciences & Engineering” and “Sustainable Process-Product Development through Green Chemistry” are two of the leading themes with papers addressing such important issues. This is complemented with additional leading themes including “Advancing the Chemical and Biological Engineering Fundamentals”, “Multi-Scale and/or Multi-Disciplinary Approach to Process-Product Innovation”, “Systematic Methods and Tools for Managing the Complexity”, and “Educating Chemical and Biological Engineers for Coming Challenges” which define the extended abstracts arrangements along this book. A total of 516 extended abstracts are included in the book, consisting of 7 invited lecturers, 15 keynote, 105 short oral presentations given in 5 parallel sessions, along with 6 slots for viewing 389 poster presentations. Full papers are jointly included in the companion Proceedings in CD-ROM. All papers have been reviewed and we are grateful to the members of scientific and organizing committees for their evaluations. It was an intensive task since 610 submitted abstracts from 45 countries were received. It has been an honor for us to contribute to setting up CHEMPOR 2008 during almost two years. We wish to thank the authors who have contributed to yield a high scientific standard to the program. We are thankful to the sponsors who have contributed decisively to this event. We also extend our gratefulness to all those who, through their dedicated efforts, have assisted us in this task. On behalf of the Scientific and Organizing Committees we wish you that together with an interesting reading, the scientific program and the social moments organized will be memorable for all.Fundação para a Ciência e a Tecnologia (FCT

Novel sustainable evaluation approach for multi-biomass supply chain

After the oil crisis held in 1973 and 1979, academicians and industry players have noticed the importance and necessity of having alternative and sustainable energy sources in future. Biological wastes, also named as “Biomass” has been cited as one of the significant sustainable energy sources. Biomass poses an ideal and substantial potential to achieve a sustainable system. However, the development of biomass industry is still relatively sluggish due to the lack of confidence of the investor to venture in this relatively new green business. This is most probably attributed to the low-maturation of biomass technologies compared to other conventional technologies, high logistics cost required for biomass transportation and uncertain market penetration barrier for the biomass-derived products. This raises the importance of having a proper biomass management system and a systematic evaluation approach to assess the sustainability performances of the biomass industry. Therefore, the ultimate goal of this thesis is to develop a sustainable multi-biomass supply chain with the aims of optimising all three sustainability dimensions simultaneously. A sustainable multi-biomass supply chain is referred as the integrated value chain of the green products, which derived from various types of biomass, starting from harvesting stage to the final products delivery stage. This thesis discusses in detail on the relevant previous research works toward the introduction of novel evaluation approach to attain different sustainable objectives (i.e., economic, environmental and social) simultaneously. The evaluation approach encompasses various components, including (i) model reduction by using P-graph integrated two-stage optimisation approach; (ii) consideration of vehicle capacity constraint for detailed transportation cost estimation; (iii) integration of various sustainability indexes using various optimisation techniques. On top of that, two novel debottlenecking approaches, one through principal component analysis (PCA) method; while another through P-graph framework, which able to identify and remove barriers that limit the sustainability performance of the biomass supply chain, are proposed. Aside from this, this thesis also aims to reduce the gaps between the researchers and industry players by developing some user-friendly and non-programming-background dependent decision-making tools. Thus, decision-makers are able to understand the insight of their problems easily without requirement of strong mathematical background. A case study in Johor, a southern state in Malaysia, which is endowed with extensive biomass resources, is used to demonstrate the effective of the proposed approaches

On the decarbonization of chemical and energy industries: Power-to-X design strategies

Tesis por compendio de publicaciones[ES]Hoy en día, la preocupación por la sostenibilidad está dando lugar a todo un nuevo sistema económico. Este nuevo paradigma afecta a todos los sectores como la agricultura, la industria, el sector financiero, etc. Dos de los más afectados son la industria química y el sistema energético debido a su configuración actual y, estos dos sectores son particularmente estudiados en esta tesis. En cuanto a la industria química, la producción electroquímica es uno de los métodos más atractivos para producir productos químicos de forma sostenible dejando atrás la producción tradicional no renovable. En esta tesis se ha prestado especial atención a la producción sostenible de amoníaco. Se han evaluado dos rutas diferentes, la primera utiliza la electrólisis del agua y evalúa diferentes tecnologías de separación del aire en función de la escala, y la segunda utiliza la biomasa como materia prima. Utilizando estos productos electroquímicos, es posible construir una nueva industria química sostenible. En esta tesis se propone la síntesis de carbo- nato de dimetilo (DMC) utilizando metanol renovable, amoníaco y dióxido de carbono capturado. En cuanto al sector energético, la introducción de fuentes renovables es esencial para alcanzar los objetivos propuestos. En este punto, el almacenamiento de energía será crucial para garantizar la satisfacción de la demanda debido a las fluctuaciones inherentes a las energías solar y eólica. Esta tesis se centra en la evaluación de productos químicos como forma potencial de almacenamiento o como vectores de energía. Se estudia la transformación del amoníaco en electricidad a escala de proceso proporcionando los resultados necesarios para implementar esta alternativa a escala de red. El diseño y el funcionamiento de las insta- laciones basadas en renovables se abordan simultáneamente, incluyendo la ubicación de las unidades debido a que los recursos renovables estan distri- buidos. Se propone un sistema integrado para utilizar productos químicos como vectores energéticos para diferentes aplicaciones energéticas en una región de España, calculando las capacidades, la operación y la ubicación óptima de las instalaciones. Además, se realiza la integración de diferentes energías renovables intermitentes y no intermitentes junto con diferentes tecnologías de almacenamiento desde una perspectiva económica y social para satisfacer una determinada demanda eléctrica. Todos estos sistemas y herramientas propuestos contribuyen a crear un escenario futuro en el que los sectores químico y energético se transforman para ser menos impactantes en el medio ambiente que nos rode