Model development and surface analysis of a bio-chemical process

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Jiang:2016:CILS,
  author =       "Dazhi Jiang and Wan-Huan Zhou and Ankit Garg and 
                 Akhil Garg",
  title =        "Model development and surface analysis of a
                 bio-chemical process",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  volume =       "157",
  pages =        "133--139",
  year =         "2016",
  ISSN =         "0169-7439",
  DOI =          "doi:10.1016/j.chemolab.2016.07.010",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0169743916301721",
  abstract =     "Phytoremediation, is a promising biochemical process
                 which has gained wide acceptance in remediating the
                 contaminants from the soil. Phytoremediation process
                 comprises of biochemical mechanisms such as adsorption,
                 transport, accumulation and translocation.
                 State-of-the-art modelling methods used for studying
                 this process in soil are limited to the traditional
                 ones. These methods rely on the assumptions of the
                 model structure and induce ambiguity in its predictive
                 ability. In this context, the Artificial Intelligence
                 approach of Genetic programming (GP) can be applied.
                 However, its performance depends heavily on the
                 architect (objective functions, parameter settings and
                 complexity measures) chosen. Therefore, this present
                 work proposes a comprehensive study comprising of the
                 experimental and numerical one. Firstly, the lead
                 removal efficiency (percent) from the phytoremediation
                 process based on the number of planted spinach,
                 sampling time, root and shoot accumulation of the soil
                 is measured. The numerical modelling procedure
                 comprising of the two architects of GP investigates the
                 role of the two objective functions (SRM and AIC)
                 having two complexity measures: number of nodes and
                 order of polynomial in modelling this process. The
                 performance comparison analysis of the proposed models
                 is conducted based on the three error metrics (RMSE,
                 MAPE and R) and cross-validation. The findings reported
                 that the models formed from GP architect using SRM
                 objective function and order of polynomial as
                 complexity measure performs better with lower size and
                 higher generalization ability than those of AIC based
                 GP models. 2-D and 3-D surface analysis on the selected
                 GP architect suggests that the shoot accumulation
                 influences (non-linearly) the lead removal efficiency
                 the most followed by the number of planted spinach, the
                 root accumulation and the sampling time. The present
                 work will be useful for the experts to accurately
                 determine lead removal efficiency based on the explicit
                 GP model, thus saving the waste of input resources.",
  keywords =     "genetic algorithms, genetic programming,
                 Phytoremediation, Lead removal, Statistical modelling,
                 Biochemical, Cross-validation",
}

Genetic Programming entries for Dazhi Jiang Wan-Huan Zhou Ankit Garg Akhil Garg

Citations