Micro-coagulation effects on direct ultrafiltration of challenging raw river water

Background The feasibility and competitiveness of substituting the conventional pre-treatment of drinking water treatment plants (dioxichlorination, coagulation/flocculation, settling, sand filtration) by raw river water direct ultrafiltration (UF) was addressed. Results A full scale UF module was operated continuously for 2 years, treating highly variable surface water. The sustainable hydraulic conditions leading to a greater water yield from the direct UF treatment scheme under different scenarios were defined. Summer periods enabled the attainment of higher filtration fluxes, although raw river water showed greater turbidity and total suspended solids content. Winter periods presented higher dissolved organic carbon concentration, with greater biopolymers content, which have been claimed as main membrane foulants. A preliminary micro-coagulation of FeCl3 (<1.5 mg Fe(III) L-1) enabled supporting harsher hydraulic conditions and thus, implementing similar conditions throughout the year. Impacts of micro-coagulation were more pronounced on filtration, particularly in winter, but a positive effect was also noticed in hydraulic and chemical cleaning stages, increasing the efficiency of the former and decreasing by half the frequency of the latter. Conclusion Direct UF proved to be competitive with the current conventional pre-treatment, leading to a significant reduction in reagents needs and sludge production and an increased and more stable product water quality. © 2016 Society of Chemical IndustryPeer ReviewedPostprint (author's final draft

Debating Evil:Using word embeddings to analyse parliamentary debates on war criminals in the Netherlands

We are proposing a method to investigate changes in historical discourse by using large bodies of text and word embedding models. As a case study, we investigate discussions in Dutch Parliament about the punishment of war criminals in the period 1945-1975. We will demonstrate how word embedding models, trained with Google's Word2Vec algorithm, can be used to trace historical developments in parliamentary vocabulary through time.</p