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Biomass to Liquids via Fischer-Tropsch synthesis (BTL-FT) is regarded as one of the most promising routes for providing alternative solution to growing demand for energy and environmental protection. In Chapter I, the development and key issues of BTL-FT process (especially Fischer-Tropsch synthesis) were reviewed and identified. In Chapter II, Mo/HZSM-5 catalyst was synthesized using Incipient Wetness Impregnation method and tested in nitrogen rich model bio-syngas. Different operation parameters (temperature, pressure, and GHSV) were tested to investigate their influence on the catalytic performance. Those parameters were found to affect the performance significantly. Liquid samples from conversion were mainly composed of C8 to C10 range hydrocarbons. The catalyst characterization revealed that molybdenum species were well distributed on the catalyst support, while dealumination, agglomeration and coke deposition were observed in spent catalyst. The top layer of the spent catalyst had the most coke deposition. A Three-Dimensionally Ordered Macro-porous (3DOM) Fe based Fischer-Tropsch catalyst was developed using a facile in-situ Nitrate Oxidation-PMMA templating technique in Chapter III. Several techniques (including SEM, BET, TPR, HRTEM, XRD, XPS, and DRIFTS) were combined to characterize the morphology, textural properties and microstructures of 3DOM Fe catalysts at different stages. The effects of bio-syngas composition on carbonaceous species formation, iron phase transformation and catalytic performance were investigated and correlated. A novel hybrid bio-refinery process co-converting biomass and natural gas into liquid fuels via FTS with a CO2 recycle loop was developed, modeled and simulated by using Aspen Plus in Chapter IV. The Aspen Plus model utilized experimental data from the 3DOM Fe catalyst. Economic analysis was performed on different scenarios based on the simulation results to determine profitability of the process. Results indicated that 102.65 t/h gasoline and 22.93 t/h diesel can be produced with the co-processing of 100.00 t/h biomass and 112.3 t/h natural gas using 307.78 t/h of recycled CO2 in the process simulation. The carbon conversion rate was estimated to be 81.23% for the hybrid process. Economic analysis revealed that the process can be profitable when using at least 10.00 t/h biomass and 11.23 t/h natural gas.

Die Vergabe von Auszeichnung und Preisen hat eine lange Tradition im Design, einhergehend mit der Kritik nach Transparenz, Motivation oder Finanzierungsmodellen seitens der Einreichenden. Dennoch erleben wir in Bezug auf Nachhaltigkeit eine erneute Aktualität. Es sprechen vielfältige Gründe dafür, und nicht zuletzt geben die Sustainable Development Goals (SDGs) nun einen Rahmen. Um global nachhaltige Strukturen zu schaffen, haben sich damit im Jahr 2015 die Mitgliedstaaten der Vereinten Nationen gemeinsame Ziele gesetzt, die in der Agenda 2030 für nachhaltige Entwicklung festgehalten sind. Diese 17 SDGs sind nicht originär der Designdisziplin zugeordnet, sondern als rahmengebend und zielsetzend für gestaltendes Handeln eines jeden zu verstehen. Einhergehend mit der Forderung nach nachhaltigem Handeln, bieten sie Kriterien, die gleiches messbar machen (sollen). Dass Design in dieser Transformation eine wesentliche Rolle spielen kann, ist kaum umstritten. Dabei geht es nicht nur um neue (oder auch wiederentdeckte) Lösungen, sondern auch um Kompetenzen und Methoden des Designs. Menschzentrierung, partizipative Prozessgestaltung und eine ganzheitliche, komplexe Lösungsentwicklung sind lediglich drei der vielfältigen Potenziale, die die Designdisziplin zu nachhaltiger Entwicklung beitragen kann. Die zahlreichen Beiträge der DGTF-Jahrestagung 2022 zum Thema ‚Design × Nachhaltigkeit‘ bilden genau dieses Spektrum ab, zeigen Hürden und er-öffnen neue, positive Perspektiven und machen Mut, diesen Weg weiter zu verfolgen.

With a growing concern of climate change due to increased levels of CO2 in the atmosphere, carbon sequestration has been suggested as a possible solution for climate change mitigation. Biochar,a highly carbonaceous product produced through pyrolysis, is considered a viable option due to its content of stable carbon. This work covers the investigation of the possibility to produce biocharfrom four different feedstocks, namely peat, straw, horse manure and sewage sludge. The study includes a literature study and a five-week trial period at a 500 kW pilot plant, PYREG 500, in Högdalen. The thermal behaviour of the feedstocks, including garden waste, was investigated using thermogravimetric analysis (TGA). The TGA results were used to decide the optimal pyrolysis temperature for peat and straw at the pilot plant. The TGA results showed that the feedstocks behave differently when pyrolysed; the mass loss rate as well as the final mass loss varied. Physiochemical characterisation of the biochar was completed and the results were in agreement with previous studies. The produced biochar from straw and two types of peat had a C content above50 wt.% (76.6, 80.7, 79.2 wt.%) and low molar ratios of H/C (0.33, 0.36, 0.38) and O/C (0.032,0.023, 0.024). The pH increased as a consequence of pyrolysis and the biochars were alkaline (pH10.1, 8.5, 8.3). Polycyclic aromatic hydrocarbons (PAHs) were found in biochar from both strawand peat (8.26, 1.03, 5.83 mg/kg). In general, nutrients and heavy metals were concentrated in the biochar, except for Cd which decreased and Hg which could not be determined. The specific surface area of biochar from straw was considered small (21 m2/g) while biochar from peat had a higher specific surface area with a greater span (102-247 m2/g). The properties of the produced biochar were compared to the criteria included in the European Biochar Certificate and some of them were fulfilled, including the content of C, PAH and heavy metals. A flue gas analysis was completed when operating the pilot plant on straw pellets and it was showed that several emissions were released, including NO2, SOX, HCl and particulates, however, solely the emissions of NO2 exceed the regulations which will be applied in 2020. Regarding process design of a future pyrolysis plant, it is suggested that the means of material transport, particle separation, temperature control and quenching of biochar should be improved.

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Global manufacturing accounted for 17% of global GDP in 2021. The heavy constructionequipment industry creates significant socio-ecological impacts through CO2 emissions, landdegradation and social risks. It is important to implement sustainability from the early phasesof product development. Research shows lack of cooperation between academia andbusinesses in testing to improve Sustainable Product Development (SPD) tools. The studyidentifies needs for implementing SPD and testing of relevant SPD tools for a heavyconstruction equipment manufacturer. It applies DSIP methodology as theoretical frameworkand focus group interviews / workshops, document content analysis and observation as datacollection methods. SAM4SIP supported in identifying the capability needs in relation toSPD implementation and informed the selection of the two SPD tools to be tested at the casecompany. First, the Leading Sustainability Criteria (LEASA) workshop generated 10measurable criteria covering all product life cycle phases which thereafter were furtherdeveloped in the Overall Sustainability Fingerprint template with respective compliancelevels to create design space. The results emphasize on the importance of taking a full-systemperspective to implement SPD on all decision levels of a company and giving opportunity tomanufacturers to utilise DSIP and find suitable tools to implement SPD.

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<p>In this dissertation, numerical analysis frameworks and software tools for digital design of process systems are developed. More specifically, these tools have been focused on digital design within the pharmaceutical manufacturing space. Batch processing represents the traditional and still predominant pathway to manufacture pharmaceuticals in both the drug substance and drug product spaces. Drug substance processes start with raw materials or precursors to produce an active pharmaceutical ingredient (API) through synthesis and purification. Drug product processes take this pure API in powder form, add excipients, and process the powder into consumer doses such as capsules or tablets.  Continuous manufacturing has allowed many other chemical industries to take advantage of real-time process management through process control, process optimization, and real-time detection of off-spec material. Also, the possibility to reduce total cleaning time of units and encourage green chemistry through solvent reduction or recycling make continuous manufacturing an attractive alternative to batch manufacturing. However, to fully understand and take advantage of real-time process management, digital tools are required, both as soft sensors during process control or during process design and optimization.  Since the shift from batch to continuous manufacturing will proceed in stages, processes will likely adopt both continuous and batch unit operations in the same process, which we will call {\em hybrid} pharmaceutical manufacturing routes. Even though these processes will soon become common in the industry, digital tools that address comparison of batch, hybrid, and continuous manufacturing routes in the pharmaceutical space are lacking. This is especially true when considering hybrid routes. For this reason, PharmaPy, an open-source tool for pharmaceutical process development, was created to address rapid in-silico design of hybrid pharmaceutical processes.  Throughout this work, the focus is on analyzing alternative operating modes within the drug substance manufacturing context. First, the mathematical models for PharmaPy's synthesis, crystallization, and filtration units are discussed. Then, the simulation capabilities of PharmaPy are highlighted, showcasing dynamic simulation of both fully continuous and hybrid processes. However, the technical focus of the work as a whole is primarily on optimization techniques for pharmaceutical process design. Thus, many derivative-free optimization frameworks for simulation-optimization were constructed and utilized with PharmaPy performing simulations of pharmaceutical processes.  The timeline of work originally began with derivative-based methods to solve mixed-integer programs (MIP) for water network sampling and security, as well as nonlinear programs (NLPs) and some mixed-integer nonlinear programs (MINLPs) for design space and feasibility analysis. Therefore, a method for process design that combines both the ease of implementation from a process simulator (PharmaPy) with the computational performance of derivative-based optimization was implemented. Recent developments in Pyomo through the PyNumero package allow callbacks to an input-output or black-box model while using {\sc Ipopt} as a derivative-based solver through the cyipopt interface. Using this approach, it was found that using a PharmaPy simulation as a black box within a derivative-based solver resulted in quicker solve times when compared with traditional derivative-free optimization strategies, and offers a much quicker implementation strategy than using a simultaneous equation-oriented algebraic definition of the problem.  Also, uncertainty exists in virtually all process systems. Traditionally, uncertainty is analyzed through sampling approaches such as Monte Carlo simulation. These sampling approaches quickly become computational obstacles as problem scale increases. In the 1980s, chemical plant design under uncertainty through {\em flexibility analysis} became an option for explicitly considering model uncertainty using mathematical programming. However, such formulations provide computational obstacles of their own as most process models produce challenging MINLPs under the flexibility analysis framework.  Specifically when considering pharmaceutical processes, recent initiatives by the FDA have peaked interest in flexibility analysis because of the so called {\em design space}. The design space is the region for which critical quality attributes (CQAs) may be guaranteed over a set of interactions between the inputs and process parameters. Since uncertainty is intrinsic to such operations, industry is interested in guaranteeing that CQAs hold with a set confidence level over a given operating region. In this work, the {\em probabilistic design space} defined by these levels of confidence is presented to address the computational advantages of using a fully model-based flexibility analysis framework instead of a Monte Carlo sampling approach. From the results, it is seen that using the flexibility analysis framework decreased design space identification time by more than two orders of magnitude.  Given implementation difficulty with new digital tools for both students and professionals, educational material was developed for PharmaPy and was presented as part of a pharmaceutical API process development course at Purdue. The students were surveyed afterward and many of the students found the framework to be approachable through the use of Jupyter notebooks, and would consider using PharmaPy and Python for pharmaceutical modeling and data analysis in the future, respectively.  Through software development and the development of numerical analysis frameworks, digital design of pharmaceutical processes has expanded and become more approachable. The incorporation of rigorous simulations under process uncertainty promotes the use of digital tools in regulatory filings and reduces unnecessary process development costs using model-based design. Examples of these improvements are evident through the development of PharmaPy, a simulation-optimization framework using PharmaPy, and flexibility analysis tools. These tools resulted in a computational benefit of 1 to 2 orders of magnitude when compared to methods used in practice and in some cases reduce the modeling time required to determine optimal operating conditions, or the design space of a pharmaceutical manufacturing process.</p>