It is of utmost importance to collect organic waste from households as a separate waste stream. If collected separately, it could be used optimally to produce compost and biogas, it would not pollute fractions of materials that can be recovered from residual waste streams and it would not deteriorate the quality of some materials in residual waste (e.g. paper). In rural areas with separate organic waste collection systems, large quantities of organic waste are recovered. However, in the larger cities, only a small fraction of organic waste is recovered. In general, citizens dot not have space to store organic waste without nuisances of smell and/or flies. As this has been the cause of low organic waste collection rates, collection schemes have been cut, which created a further negative impact. Hence, additional efforts are required. There are some options to improve the organic waste recovery within the current system. Collection schemes might be improved, waste containers might be adapted to better suit the needs, and additional underground organic waste containers might be installed in residential neighbourhoods. There are persistent stories that separate organic waste collection makes no sense as the collectors just mix all municipal solid waste after collection, and incinerate it. Such stories might be fuelled by the practice that batches of contaminated organic waste are indeed incinerated. Trust in the system is important. Food waste is often regarded as unrein. Users might hate to store food waste in their kitchen that could attract insects, or the household pets. Hence, there is a challenge for socio-psychological research. This might also be supported by technology, e.g. organic waste storage devices and measures to improve waste separation in apartment buildings, such as separate chutes for waste fractions. Several cities have experimented with systems that collect organic wastes by the sewage system. By using a grinder, kitchen waste can be flushed into the sewage system, which in general produces biogas by the fermentation of sewage sludge. This is only a good option if the sewage is separated from the city drainage system, otherwise it might create water pollution. Another option might be to use grinders, that store the organic waste in a tank. This tank could be emptied regularly by a collection truck. Clearly, the preferred option depends on local conditions and culture. Besides, the density of the area, the type of sewage system and its biogas production, and the facilities that are already in place for organic waste collection are important parameters. In the paper, we will discuss the costs and benefits of future organic waste options and by discussing The Hague as an example.
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This is a repository containing an agent-based model (code, data, documents and results) of Industrial Symbiosis Network implementation. The repository is related to the publication:Lange, K.P., Korevaar, G., Nikolic, I., Herder, P.M., 2021. Actor Behaviour and Robustness of Industrial Symbiosis Networks: An Agent-Based Modelling Approach, 2020:64:4. JASSS. https://doi.org/10.18564/JASSS.4635The purpose of the model is to explore the influence of actor behaviour, combined with environment and business model design, on the survival rates of Industrial Symbiosis Networks (ISN), and the cash flows of the agents. We define an ISN to be robust, when it is able to run for 10 years, without falling apart due to leaving agents.The model simulates the implementation of local waste exchange collaborations for compost production, through the ISN implementation stages of awareness, planning, negotiation, implementation, and evaluation.One central firm plays the role of waste processor in a local composting initiative. This firm negotiates with other firms to become a supplier of their organic residual streams. The waste suppliers in the model can decide to join the initiative, or to have the waste brought to the external waste incinerator. The focal point of the model are the company-level interactions during the implementation or ending of synergies.The model consists of three types of actors, waste suppliers, processor, and incinerators. The modeled waste supplier and processor are part of the ISN. In the model these agent types negotiate and evaluate the outcomes by means of the Theory of Planned Behavior. The modeled incinerator is part of the external environment. This agent acts as the infinite sink of all waste flows, taking up op the waste that is not used in the local composting initiative.
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This study proposes a systematic value chain approach to helping businesses identify and eliminate inefficiencies. The authors have developed a robust framework, which food-sector entrepreneurs can use to increase profitability of an existing business or to create new profitable opportunities. The value chain approach provides win-win opportunities for players within the value chain. To test the robustness of the framework, the authors use food waste as an example of a critical inefficiency and apply it to two different food sector business cases, each operating in diverse conditions. Because the suggested framework addresses the core elements and parameters for the existence and competitiveness of a business, the model can be adapted to other sectors.
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In this proposal, a consortium of knowledge institutes (wo, hbo) and industry aims to carry out the chemical re/upcycling of polyamides and polyurethanes by means of an ammonolysis, a depolymerisation reaction using ammonia (NH3). The products obtained are then purified from impurities and by-products, and in the case of polyurethanes, the amines obtained are reused for resynthesis of the polymer. In the depolymerisation of polyamides, the purified amides are converted to the corresponding amines by (in situ) hydrogenation or a Hofmann rearrangement, thereby forming new sources of amine. Alternatively, the amides are hydrolysed toward the corresponding carboxylic acids and reused in the repolymerisation towards polyamides. The above cycles are particularly suitable for end-of-life plastic streams from sorting installations that are not suitable for mechanical/chemical recycling. Any loss of material is compensated for by synthesis of amines from (mixtures of) end-of-life plastics and biomass (organic waste streams) and from end-of-life polyesters (ammonolysis). The ammonia required for depolymerisation can be synthesised from green hydrogen (Haber-Bosch process).By closing carbon cycles (high carbon efficiency) and supplementing the amines needed for the chain from biomass and end-of-life plastics, a significant CO2 saving is achieved as well as reduction in material input and waste. The research will focus on a number of specific industrially relevant cases/chains and will result in economically, ecologically (including safety) and socially acceptable routes for recycling polyamides and polyurethanes. Commercialisation of the results obtained are foreseen by the companies involved (a.o. Teijin and Covestro). Furthermore, as our project will result in a wide variety of new and drop-in (di)amines from sustainable sources, it will increase the attractiveness to use these sustainable monomers for currently prepared and new polyamides and polyurethanes. Also other market applications (pharma, fine chemicals, coatings, electronics, etc.) are foreseen for the sustainable amines synthesized within our proposition.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
