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District heating potential in the case of low-grade waste heat recovery from energy intensive industries

Abstract Waste Heat Recovery (WHR) from energy intensive industries has a great potential in curbing CO2 emissions. Among the different solutions, District Heating (DH) is considered of major interest, satisfying the heating demand of users in the proximity of power plants. Considering the energy intensity of the pulp and paper industry, a method for evaluating the recovery potential of its low-grade waste heat from cogeneration plants in DH is presented. The proposed method allows to evaluate the thermal power from cogeneration plants to end users and to assess the potential maximum number of residential buildings that could be connected to each DH network. Based on the proposed method, the benefits of the WHR are evaluated from both energy and environmental points of view. More precisely, considering 50 pulp and paper mills in Italy under investigation in the present analysis, a yearly natural gas saving corresponding to 143.76 kTonnes of Oil Equivalent (TOE) and 333.11 ktCO2 is obtained. In case of WHR, the average Primary Energy Saving (PES) of the cogeneration plants increases from 0.14 up to 0.22. In particular, cogeneration units based on steam turbine technology show the greatest improvement, since its average PES moved from 0 up to almost 0.1.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).31 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
