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Faculty of Engineering and Built Environment

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Subject: search.filters.subject.Flotation

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    Response surface optimization of oil refinery wastewater treatment process
    (GRAWW, 2019-03) Rathilal, Sudesh; Tetteh, Emmanuel Kweinor; Assis, Shan
    In this paper, a laboratory dissolved air flotation (DAF) process was employed for the removal of chemical oxidation demand (COD), soap oil and grease (SOG), total suspended solids (TSS) and turbidity from oil refinery wastewater (ORW) using polyferric sulfate. The optimization was carried out by response surface methodology Box-Behnken design to evaluate the interactive effects of three main independent process parameters (pH, coagulant dosage and flotation time) on the removal of the COD, SOG, TSS, and turbidity. The quadratic model fitted very well with the experimental data at regression coefficients (R2) of values of 0.9986; 0.9992; 0.9847; 0.9858 for COD, SOG, TSS, and turbidity respectively. Under the optimum conditions of coagulant dose of 48 mgL-1, pH (5) and flotation time (17 min), the maximum removal of COD, SOG, TSS, and turbidity were 86%, 92%, 84% and 85% respectively were obtained. The removal efficiencies showed a high significance of the model correlations at 95% confidence level. This demonstrated that the addition of the polymeric sulfate can enhance the treatability performance of the ORW.
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    Neural network modelling and prediction of the flotation deinking behaviour of industrial paper recycling processes
    (Nordic Pulp & Paper Research Journal, 2014) Pauck, Walter James; Venditti, Richard; Pocock, Jon; Andrew, Jerome Edward
    The removal of ink from recovered papers by flotation deinking is considered to be the “heart” of the paper recycling process. Attempts to model the deinking flotation process from first principles has resulted in complex and not readily usable models. Artificial neural networks are adept at modelling complex and poorly understood phenomena. Based on data generated in a laboratory, artificial neural network models were developed for the flotation deinking process. Representative samples of recycled newsprint, magazines and fine papers were pulped and deinked by flotation in the laboratory, under a wide variety of practical conditions. The brightness, residual ink concentration and the yield were measured and used to train artificial neural networks. Regressions of approximately 0.95, 0.85 and 0.79 respectively were obtained. These models were validated using actual plant data from three different deinking plants manufacturing seven different grades of recycled pulp. It was found that the brightness and residual ink concentration could be predicted with correlations in excess of 0.9. Lower correlations of ca. 0.43 were obtained for the flotation yield. It is intended to use the data to develop predictive models to facilitate the management and optimization of commercial flotation deinking processes with respect to recycled paper inputs and process conditions.