Formation of ferric flocks to remove for the removal of Zn and Cu from dockyard wastewater

Wastewater from wash down of boat hulls contains typically Cu, Zn and organometallic biocides, e.g. tributyltin (TBT). In some cases this wastewater is led directly into the marine system. In the present paper, a cheap flocculation method (iron flocculants) for removal of Cu and Zn from the wastewater is investigated and the method was shown successful. TBT concentration in a sample from a German dockyard was also decreased during flocculation, but the TBT removed was already associated with small particulates in the original sample. Low concentrations of heavy metals were obtained by flocculation: <0.05 mg Cu/l and 0.2 mg Zn/l. An erratum to this article can be found at     

Bioethanol from waste: Life cycle estimation of the greenhouse gas saving potential

This paper considers two alternative feedstocks for bioethanol production, both derived from household waste—Refuse Derived Fuel (RDF) and Biodegradable Municipal Waste (BMW). Life Cycle Assessment (LCA) has been carried out to estimate the GHG emissions from bioethanol using these two feedstocks. An integrated waste management system has been considered, taking into account recycling of materials and production of bioethanol in a combined gasification/bio-catalytic process. For the functional unit defined as the ‘total amount of waste treated in the integrated waste management system’, the best option is to produce bioethanol from RDF—this saves up to 196 kg CO₂ equiv. per tonne of MSW, compared to the current waste management practice in the UK.However, if the functional unit is defined as ‘MJ of fuel equiv.’ and bioethanol is compared with petrol on an equivalent energy basis, the results show that bioethanol from RDF offers no saving of GHG emissions compared to petrol. For example, for a typical biogenic carbon content in RDF of around 60%, the life cycle GHG emissions from bioethanol are 87 g CO₂ equiv./MJ while for petrol they are 85 g CO₂ equiv./MJ. On the other hand, bioethanol from BMW offers a significant GHG saving potential over petrol. For a biogenic carbon content of 95%, the life cycle GHG emissions from bioethanol are 6.1 g CO₂ equiv./MJ which represents a saving of 92.5% compared to petrol. In comparison, bioethanol from UK wheat saves 28% of GHG while that from Brazilian sugar cane – the best performing bioethanol with respect to GHG emissions – saves 70%. If the biogenic carbon of the BMW feedstock exceeds 97%, the bioethanol system becomes a carbon sequester. For instance, if waste paper with the biogenic carbon content of almost 100% and a calorific value of 18 MJ/kg is converted into bioethanol, a saving of 107% compared to petrol could be achieved. Compared to paper recycling, converting waste paper into bioethanol saves 460 kg CO₂ equiv./t waste paper or eight times more than recycling.     

Estimating uncertainties in the life cycle assessment of composting household biowaste and urban green waste in Germany

Life cycle assessment (LCA) of waste treatment processes is often associated with considerable uncertainties. The aim of this study is to estimate the total uncertainty in the modelled composting system and the influence of material and process parameters on the uncertainty. Four composting combinations with fresh (FC) and mature substrate compost (MSC) from partially enclosed (PEC) and open composting (OC) were investigated. Perturbation analysis was used to determine the effect of parameters on the result and Monte Carlo simulation was used to estimate the total uncertainty. This study showed that the production of MSC using PEC had the lowest overall impacts across all impact categories except ozone depletion. Results of the Monte Carlo simulation showed that comparing composting options was challenging. The sensitivity ratios obtained from the perturbation analysis showed that the process parameter percentage of carbon fraction degraded was the most influential for FC. In MSC, the moisture content in the input material and the substitution factor used for peat were the most influential. Monte Carlo simulations demonstrated the overall uncertainty of the model and its relevance when comparing results between combinations. The perturbation analysis identified the parameters that required more accurate data to reduce the uncertainty in the model.

Graphical abstract