Archives

  • 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-06
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • 2024-04
  • 2024-05
  • 2024-06

    • Leaching process is one of the

    2018-11-05

    Leaching process is one of the most frequently employed techniques in extractive metallurgy for the recovery of valuable metals using aqueous solutions. Low cost implication, eco-friendly procedure (Habbache et al., 2009), low energy requirement and ability to treat low grade ores (Ajemba and Onukwuli, 2012a) are among attractive features which make its application to hydrometallurgy important. It is in line with this that most researches on heavy metal extraction employed the process of cox 2 inhibitors leaching (Sultana et al., 2012; Hernandez et al., 2012; Habbache et al., 2009). Acid leaching involves using any suitable acid leachant on different substrates to extract heavy metal ores contained in the clay. The basic reaction for the process is given by: Azaraegbelu clay has been assessed to be commercially available in Eastern region of Nigeria. There is no published data dealing with this clay sample in the aluminium industry. Moreover, previous studies on alumina dissolution seem to rely mostly on one factor at a time (OFAT) approach. Such method has been proven by research to be deficient, unsatisfactory and time consuming (Uzohet al, 2013). Response surface methodology (RSM) has been widely adopted in industries such as drug and food, chemical and biological processes, for the purpose of either producing high quality products or operating the process in a more cost effective manner and ensuring the process operates in a more stable and reliable way (Sudamalla et al., 2012). It has also been successfully applied to different processes for achieving its optimisation using experimental designs (Gunawan and Suhendra, 2008; Alam et al., 2007; Narayana et al., 2011). The study therefore, is intended to; explore the most significant factors and their possible interactions which influence the overall efficiency of the dissolution of alumina from Azaraegbelu clay; characterise their effects and predict the possible route(s) to the desired optimal; evaluate and compare the dissolution efficiency of the clay using ANN and RSM. An optimisation problem which can be conveniently solved through the well-known RSM is thus formulated. Such method often requires fitting a predictive model to the data obtained from few experiments through regression analysis. It enables easy approximation to a desired surface by analysing the associated response surface diagrams (or contours) derived from such models, hence obviating the need for experimenting in an ad hoc manner in search of the desired response(s). Overall, this new alumina dissolution design will guarantee increased overall process efficiency through;
    Material and methods
    Results and discussion
    Conclusion The aim of the present study was to investigate the optimum conditions for the extraction of alumina from Azaraegbelu clay. RSM and ANN methodologies were used for modelling and prediction of the process parameters. ANOVA analysis showed a significant curvature in the design space and quadratic models were performed for the extraction of alumina. Multi-layer neural network (5-6-1) was chosen to develop accurate and complex non-linear relationships. RSM and ANN regression coefficients were obtained to be 0.9582 and 0.9726 respectively showing very good agreement with the experimental data. Neural network showed better efficiency and accuracy than RSM. Both two methodologies have proved to be quick and useful procedures for the investigation and optimisation of alumina recovery from Azaraegbelu clay. The process optimisation was performed using RSM and the quadratic model in terms of the actual factors obtained was;
    Introduction Wood plastic composite (WPC) materials are fast growing into a material of choice to complement timber for applications in the building and construction industry (Klyosov, 2007; Wechsler and Hiziroglu, 2007). Generally, WPCs constitute a blend of plastics (high-density polyethylene or polypropylene) and wood dust as reinforcement filler. The interface between the non-polar plastic and the polar wood dust is improved by a coupling agent which ensures the transfer of stress between the two phases when a load is applied on the composite material. Coupling agents provide a chemical interaction between the polymer (plastic) and the reinforcement material (wood dust) thus improving the adhesion and dispersability between the otherwise not very compatible materials which consequently results in enhanced physical and mechanical properties of the composite. Typical coupling agents used in the process include maleated polyolefins (maleated polypropylene or maleated polyethylene), anhydrides or organic acids (Klyosov, 2007).