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The potential beneficial and harmful social impacts generated by the introduction of novel technologies, in general, and those concerning nutrient recovery and the improvement of nutrient efficiency in agriculture, in particular, have received little attention, as shown in the literature. This study investigated the current social impacts of agricultural practices in Belgium, Germany and Spain, and the potential social impacts of novel technologies introduced in agriculture to reduce nutrient losses. Based on 65 indicators used in the PSILCA database, the greatest impacts in the baselines are related to fair salaries, biomass consumption, industrial water depletion and public sector corruption. The potential social impacts of the technologies were assessed using 17 midpoint indicators that have a potential to affect social endpoints. The potential benefits of novel agricultural technologies were the creation of more attractive jobs in agriculture, and a better and healthier environment for local communities, workers and society. However, their harmful effects mainly related to workers and local community health, due to the substances used in the technologies and the potential gases emitted. Given the current lack of Social Life Cycle Assessment (S-LCA) studies on novel technologies in agriculture, this study is the first to use the PSILCA database to assess different technologies for nutrient recovery in agriculture in an initial and prospective assessment of their potential social impacts. Further work is required for a site-specific assessment of the technologies when a higher level of social adaptation is achieved. info:eu-repo/semantics/publishedVersion

The heat exchanger network (HEN) synthesis problem can be characterized as non-convex, non-linear and highly combinatory. Mainly for large-scale HEN, the complexity increases even more and mathematical formulation may require very sophisticated methods. In this work, the simultaneous synthesis problem is solved by a bi-level hybrid method: in the upper level a Genetic Algorithm (GA) is used to optimized discrete variables and in the lower level, the continuous variables are optimized by Differential Evolution (DE). Two examples from the literature were used to test the applicability of the proposed solution and results found in terms of the total annual cost (TAC) were lower than those published in the literature.

The adverse effects of agriculture and livestock production on the environment are well-known and require mitigation in order to achieve sustainability in the food production chain. This study focused on adverse effects related to biogeochemical flows of phosphorus and nitrogen cycles which natural balances have been greatly disturbed by current practices. To assess the potential benefits and detrimental effects of proposed mitigation measures, adequate impact indicators are required. The challenge lies in identifying and providing indicators that cover the important aspects of environmental sustainability and allow a direct comparison of policy alternatives. A review of potential indicators that are also consistent with those used to indicate the performance of agricultural and general sustainability (i.e. the European Green Deal) led to the selection of fifteen agrienvironmental indicators covering the main environmental issues in agriculture. The indicators identified offered an effective representation of environmental behaviour and would be useful in communicating a comprehensive ‘dashboard’ for professional end users of solutions to nutrient recovery and nutrient efficiency improvement in arable and livestock systems. The selected dashboard indicators (DBI) covered the dimensions of ‘use of primary resources’, ‘emissions to the environment’ and ‘resilience to climate change’. Five case studies were investigated to test the DBI using an Excel questionnaire applying the qualitative approach of the Delphi method together with expert knowledge. As expected, the results indicated that there were potential benefits of the technologies in terms of improved ‘nutrient recovery’ and decreased ‘nitrate leaching’. Potential disadvantages included increased electricity and oil consumption and greater ammonia volatilisation due to the increased use of organic fertilisers. The indicator ‘water’ received more neutral responses; thus, the specific technology was not expected to consistently affect the indicator. In relation to ‘particulate matter’, the results were indicated to be ‘unknown’ for some solutions due to the difficulty of predicting this indicator. Furthermore, methodologies for estimating quantitative values for the dashboard indicators were proposed, and a quantitative assessment was performed for the solution ‘catch crops to recover nutrients’, confirming the responses in the qualitative assessment. The dashboard indicators selected covered the main aspects of the solutions, identified in more comprehensive studies of environmental impacts, as being suitable for the rapid assessment of technologies for nutrient recovery in agriculture. As such, they can be used as a pre-screening m