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In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.

This study introduces an innovative methodology for early-stage biorefinery design and analysis through the integration of metabolic models into superstructure optimization. The focus is on two types of metabolic models: i) those where ideal growth conditions are assumed, and ii) those where growth is dependent on environmental variables. Metabolic models offer a comprehensive and dynamic representation of the bioreactor system, unbound by the limitations of traditional kinetic models or the need of extensive experimental data. The integration of these models involves a curation and validation process, ensuring that the metabolic capabilities of the microorganisms are accurately represented. Once validated, the models are plugged into the superstructure in the form of surrogate equations that can be handled by the optimization problem for the superstructure. The effectiveness of this methodology is then tested and critically analyzed with the help of two case studies. The primary contribution of this work lies in its effective identification of the most favorable combinations of substrates and microorganisms. The outcome establishes the feasibility of utilizing different substrates and microorganisms in a biorefinery context, thereby highlighting the value of metabolic models in a superstructure optimization framework for exploring early-stage biorefinery schemes. This approach holds significant promise in enhancing the efficiency and sustainability of future biorefinery systems.

USABLE Packaging project has developed an innovative value chain based on mixed microbial cultures using organic wastes as feedstock to produce prototypes of PHA-based items that were compared to their commercial counterparts, so the expected better environmental performance can be checked. To do so, a cradle-to-grave life cycle assessment was carried out. The system was modeled integrating all available knowledge, from pilot-scale data to process simulators, and following upscaling frameworks. PHA-based items outperform their commercial counterparts since they show environmental benefits thanks to the avoided electricity obtained in the cogeneration heat and power unit within the PHA production; however, these environmental benefits are very sensitive to the substituted electricity environmental burdens. The different levels of development and market implementation are seen as critical and therefore attention should be paid on how upscaling affects the results and interpretation of the LCA while serving as a guide for process and product development

Utilities produce and store vast amount of data related to urban wastewater management. Not yet fully exploited, proper data analysis would provide relevant process information and represents a great opportunity to improve the process performance. In the last years, several statistical tools and benchmarking methods that can extract useful information from data have been described to analyse wastewater treatment plant (WWTP) energy efficiency. Improving energy efficiency at WWTPs is however a complex task which involves several actors (both internal and external to the water utility), requires an exchange of different types of information which can be analysed by a broad selection of methods. Benchmarking method therefore must not only be selected based on whether they provide a clear identification of inefficient processes; it must also match the available data and the skills of those performing the assessment and objectives of stakeholders interpreting the results. Here, we have identified the requirements of the most common benchmarking methods in terms of data, resources, complexity of use, and information provided. To do that, inefficiency is decomposed so that the analyst, considering the objective of the study and the available data, can link each element to the appropriate method for quantification and benchmarking, and relate inefficiency components with root-causes in wastewater treatment. Finally, a framework for selecting the most suitable benchmarking method to improve energy efficiency in WWTPs is proposed to assist water sector stakeholders. By offering guidelines on how integrates and links data, methods and actors in the water sector, the outcomes of this article are expected to move WWTPs towards increasing energy efficiency

The production of volatile fatty acids (VFA) is gaining momentum due to their central role in the emerging carboxylate platform. Particularly, the production of the longest VFA (from butyrate to caproate) is desired due to their increased economic value and easier downstream processing. While the use of undefined microbial cultures is usually preferred with organic waste streams, the use of defined microbial co-culture processes could tackle some of their drawbacks such as poor control over the process outcome, which often leads to low selectivity for the desired products. However, the extensive experimentation needed to design a co-culture system hinders the use of this technology. In this work, a workflow based on the combined use of mathematical models and wet experimentation is proposed to accelerate the design of novel bioprocesses. In particular, a co-culture consisting of Pediococcus pentosaceus and Megaphaera cerevisiae is used to target the production of high-value odd- and even‑carbon VFA. An unstructured kinetic model was developed, calibrated and used to design experiments with the goal of increasing the selectivity for the desired VFA, which were experimentally validated. In the case of even‑carbon VFA, the experimental validation showed an increase of 38 % in caproate yield and, in the case of enhanced odd‑carbon VFA experiments, the yield of butyrate and caproate diminished by 62 % and 94 %, respectively, while propionate became one of the main end products and valerate yield value increased from 0.007 to 0.085 gvalearte per gconsumed sugar. The workflow followed in this work proved to be a sound tool for bioprocess design due to its capacity to explore and design new experiments in silico in a fast way and ability to quickly adapt to new scenarios

Climate change is already affecting and will continue to affect European dairy farms, the most climate-vulnerable stage in them dairy value chains. Climate hazards and their biophysical impacts are expected to modify dairy farms' environmental performance unevenly across regions and climate scenarios by 2050. The integration of tools and methodologies, such as Life Cycle Assessment, Risk Assessment, and Input-Ouutput tables, is crucial to address this challenge. Thus, a toolbox is developed and tested in this research to provide an in-depth analysis of the biophysical impacts that climate hazards pose to the environmental performance of the sector. This research is a stepping stone in providing a scientific basis to the European dairy sector, so it can support decision-making to begin building adaptative capacity.

The development of biobased and circular value chains entails multiple opportunities and challenges and involves multiple actors across the value chain with different interests and available knowledge. The PHA value chain based on mixed microbial culture systems could be depicted as a sort of puzzle, whose different pieces are assembled in this thesis by integrating the available knowledge, tools, and methodologies. This thesis assisted the decision-making during the development of this innovative value chain at multiple levels: from solving the local bottlenecks (zoom in) within the anaerobic fermentation and the PHA downstream processing to forecast the development of waste-to-PHA biorefineries and developing a framework (zoom out) that connects stakeholders across the value chain.