Photovoltaic Solar Electrochemical Oxidation (PSEO) For Treatment Of Lignosulfonate Wastewater

 Introduction Industrial wastewater treatment, such as those used for tannery wastewater, is complex due to the variety of chemicals added at different stages of processing of hides and skins. Major problems in tanneries are due to wastewater containing heavy metals, toxic chemicals, chloride, lime with high dissolved and suspended salts and other pollutants (Uberoi, 2003). The tanning process and the effluents generated have already been reported in literature (Wiegant et al., 1999, Sreeram and Ramasami, 2003, Stoop, 2003). Many conventional processes were carried out to treat wastewater such as biological process (Ahn et al., 1996, Vijayaraghavan and Murthy, 1997, Wiemann et al., 1998, Di Iaconi et al., 2003, Farabegoli et al., 2004), oxidation process (Sekaran et al., 1996, Dogruel et al., 2004, Sacco et al., 2012, de Caprariis et al., 2012) and chemical process (Di Iaconi et al., 2001, Orhon et al., 1998, Song et al., 2004) etc. Among these, physical and chemical methods are considered very expensive in terms of energy and reagents consumption (Churchley, 1994, Stern et al., 2003), and generation of excessive sludge (Chu, 2001). To reduce the production of sludge by the treatment of this wastewater combined or alternative systems must be explored. In particular, in this work, two spiral wound membrane modules were used: nanofiltration (NF) and reverse osmosis (RO). The goal of this approach is to insert membranes into the cycle of wastewater treatment in order to remove the entire chain of biological treatment and the resulting post physico[1]chemical residue with a significant reduction of sludge up to 95%. A modified version of the traditional method used to measure critical fluxes of membranes, that is the pressure cycling method, was applied to measure both the critical and the threshold flux on the nanofiltration membrane in order to optimize the operating conditions. Once obtained the critical and threshold flux values, this data was used as input for a batch membrane process optimization method developed previously by Stoller at al. (Stoller and Chianese, 2006, Stoller and Bravi, 2010, Stoller, 2009, Iaquinta et al., 2009, Stoller, 2008, Stoller, 2011). The output of the method indicates the optimal permeate feed flow rate which should be used during the batch in order to inhibit membrane fouling. Finally, the obtained results were compared from an economical point of view with a conventional biological process to validate the membrane plant as possible alternative to conventional process.

Abstract: The integration of artificial intelligence into various aspects of daily life is developing at a rapid pace in Japan. Discussions to govern applications of artificial intelligence to the field of social infrastructure are also critical and need to match the rapid pace of development. However, the legal implications and risks of applying artificial intelligence to the management of lifelines such as drinking water supply and wastewater treatment have not yet been fully explored. This paper reviews the existing legislations and ongoing discussions on governance regarding applications of artificial intelligence to water and wastewater management. Based on the review, we discuss the ability of legislative frameworks in Japan to respond to the applications of artificial intelligence, as well as identifying potential gaps and challenges thereof, including access to accurate data, demarcation of rights and responsibilities, risk hedging and risk management, monitoring and evaluation, and handling of intellectual property rights. This paper concludes with key recommendations to national and local governments to support the application of artificial intelligence in the field of water and wastewater.

The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. Additionally, to anticipate changes in groundwater WQI, IBM® SPSS® Statistics 19 software (SPSS) was used to develop an artificial neural network model (ANNM). With the application of this ANNM model, the results obtained illustrated high prediction efficiency, as the sum of squares error functions (for training and testing samples) and coefficient of determination (R2), were found to be (0.038 and 0.005) and 0.973, respectively.