The search for new materials that replace fossil fuel-based plastics has been focused on biopolymers with similar physicochemical properties to fossil fuel-based plastics, such as Polyhydroxyalkanoates (PHA). The present paper reviews the challenges of scaling-up PHA production from waste streams during the period from 2014 to 2016, focusing on the feasibility of the alternatives and the most promising alternatives to its scaling-up. The reviewed research studies mainly focus on reducing costs or obtaining more valuable polymers. In the future, the integration of PHA production into processes such as wastewater treatment plants, hydrogen production or biodiesel factories could enhance its implementation at industrial scale.

The rapid growth of agricultural data necessitates the development of storage systems that are scalable and efficient in storing, retrieving and analyzing very large datasets. The traditional relational database management systems (RDBMSs) struggle to keep up with large-scale analytical queries due to the volume and complexity inherent in those data. This study presents the design and implementation of a scalable data warehouse (DWH) system for agricultural big data. The proposed solution efficiently integrates data and optimizes data ingestion, transformation, and query performance, leveraging a distributed architecture based on HDFS, Apache Hive, and Apache Spark, deployed on dockerized Ubuntu Linux environments. This paper highlights the reasons why a DWH is irreplaceable for big data processing, without disputing the strengths of traditional databases in transactional use cases. By detailing the architectural choices and implementation strategy, this study provides a practical framework for deploying robust DWH solutions that are useful in supporting agricultural research, market predictions and policy decision-making.

Medium size wastewater treatment plants are considered too small to implement anaerobic digestion technologies and too large for extensive treatments. A promising option as sewage sludge reduction method is the inclusion of anoxic time exposures. In the present study, three different anoxic time exposures of 12, 6 and 4 hours have been studied to reduce sewage sludge production. The best anoxic time exposure was observed under anoxic/oxic cycles of 6 hours, which reduced 29.63% of the biomass production compared with the oxic control conditions. The sludge under different anoxic time exposures, even with a lower active biomass concentration than the oxic control conditions, showed a much higher metabolic activity than the oxic control conditions. Microbiological results suggested that both protozoa density and abundance of filamentous bacteria decrease under anoxic time exposures compared to oxic control conditions. The anoxic time exposures 6/6 showed the highest reduction in both protozoa density, 37.5%, and abundance of filamentous bacteria, 41.1%, in comparison to the oxic control conditions. The groups of crawling ciliate, carnivorous ciliates and filamentous bacteria were highly influenced by the anoxic time exposures. Protozoa density and abundance of filamentous bacteria have been shown as a promising bioindicators to biomass production reduction.

The current paper describes the work carried out in the Horizon 2020 AFTERLIFE project – "Advanced Filtration TEchnologies for the Recovery and Later conversIon of relevant Fractions from wastEwater" – (Grant Agreement no. 745737) which focuses on bioprocess modelling and optimisation using computational tools. The project addresses the development of a flexible, cost- and resource-efficient process framed in the zero-waste and circular economy approach for the recovery and valorisation of the relevant fractions from wastewater. The first step of such a process is an initial step consisting of a cascade of membrane filtration units to separate the total solids in sewage. Then, the concentrates recovered in each unit will be treated to obtain high-pure extracts and metabolites or to be converted into value-added biopolymers (polyhydroxyalkanoates). Moreover, the outflow of the process is an ultra-pure water stream that can be directly reused. Following a holistic approach, the design and optimisation of the AFTERLIFE process will improve performance and reduce the costs associated with wastewater treatment by maximising the value recovery. The paper focuses on the work done developing and implementing computational tools to model and optimise the design of the process. A framework for modelling-based optimisation has been developed. The applied optimisation approach is not computationally demanding and can be systematically applied to different processes. Finally, a use case establishing a scenario for testing the developed framework is described. The defined process model and optimisation methodology were applied to simulate the treatment of wastewater from the fish processing industry. The performance of the optimisation tool is analysed considering the simulation results.

Agricultural activities and food industry have increased as a consequence of the population growth in the world. Each year these activities generate a huge volume of residues as a result of farming operations (harvesting and/or pruning, etc.), processing in food industry and daily human consumption. Normally, the accumulated agricultural residues during farming operations do not have any function in the food industry. It is also common to see the collection of by-products (husks, hulls, seeds, peel, shell, skin, etc.) during the raw material transformation in the operations (washing, processing, preparing and/or packaging, etc.) which constitute the process. The agro-food residue is directly linked to daily life and has important environmental, economic, and social impacts. All these residues can be reused or revalorized (crude, modified, and carbonized) as bioadsorbents for organic and inorganic pollutants contained in wastewater.