Elsevier

Journal of Cleaner Production

Volume 161, 10 September 2017, Pages 860-870
Journal of Cleaner Production

Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming

https://doi.org/10.1016/j.jclepro.2017.05.192Get rights and content

Highlights

  • The adsorption of phenols by activated carbon is automatically modeled.

  • Genetic programming is applied for the first time on this real-world problem.

  • Genetic programming with local search achieves the best performance.

Abstract

The process of adsorption of phenols and nitrophenols by activated carbon is one of the most important types of wastewater treatment. However, there is a lack of a general analytic method to predict the adsorption efficiency under different operating conditions. This work studies a data driven approach towards modeling the adsorption process, taking as input the type of contaminant, the pH level, the initial concentration and the elapsed time, in order to predict the adsorption efficiency. In particular, this work is the first to use genetic programming (GP), an evolutionary computation paradigm for automatic program induction, to address the stated modeling problem. Two recently proposed GP algorithms are used and compared with other regression techniques, using real-world experimental data collected under typical operating conditions. Results show that GP enhanced with a local search operator (GP-LS) achieves the best results relative to all other methods, achieving a median performance of MSE=94.14, R2=0.92 and average solution size of 41 nodes. Therefore, this technique constitutes a promising framework for the automatic modeling of the adsorption efficiency.

Introduction

Currently, the exponential growth of global population and the manner in which industrial societies are developing, have given rise to serious environmental problems. For instance, the intensive development of the chemical industry, that has been required to meet the growing demand, has produced large scale pollution from coal processing (Song et al., 2015) and carbon emissions (Song and Zhou, 2015); global issues with significant economic, social and environmental implications (Lee et al., 2016, Jaromir and Varbanov, 2016). For these reasons, multidisciplinary research and collaboration between stakeholders should be enhanced to stimulate the transition toward a more sustainable society (Niesten et al., 2016).

Regarding our water supply, the excessive applications of phenolic compounds leads to serious repercussions. Water is widely used as a raw material in the process industries for a wide range of products. In particular, phenol and phenolic compounds are common contaminants found in effluents from the production of plastics, leather, paint, textile, paper, printing and other petrochemical processes (Altenor et al., 2009). The wastewaters from these industries are generating considerable amounts of phenolic pollutants (Kumar and Jena, 2016), leading to increasingly stringent environmental regulations on the discharge of effluents (Klemeš et al., 2011). The main reason for this is that these contaminants are extremely toxic, even if they are present in small concentrations their impact on health include protein degeneration, tissue erosion, paralysis of the central nervous system, vomiting, difficulty in swallowing, anorexia, liver and kidney damage, headache, fainting and various mental disturbances (Qadeer and Rehan, 2002, Srivastava et al., 2006, Takahashi et al., 1994). In fact, phenolic compounds are classified as high priority pollutants due to their carcinogenic effect on human health and their harmful effects on wild life (Altenor et al., 2009). Therefore, it is necessary to remove these phenolic compounds from industrial effluents before discharging them into the water stream.

The US Environmental Protection Agency (EPA) has instituted a regulation for lowering phenol concentration in the wastewater to less than 1.0 mg/dm3 (Tian et al., 2007). Therefore, some researchers have proposed a variety of methods for their removal from wastewater (Ukrainczyk and McBride, 1992). Among current proposals, the removal of phenols by adsorption technologies is regarded as one of the best methods because it does not require a high operating temperature and can be considered to be a relatively simple process from an implementation standpoint. Indeed, various adsorbent solids have been used to remove phenolic compounds from wastewater, including activated carbon (Nouri et al., 2002), silica (K1 et al., 2002), polymeric resins (Abburi, 2003), fly ash (Sarkar et al., 2005) and kaolinite (Barhoumi et al., 2003).

This work studies the use of activated carbon for the adsorption of phenols and nitrophenols. In particular, the goal of this paper is find models that capture the nonlinear relationships between several relevant operating conditions, such as the contaminant, initial concentration, pH and contact time, to predict the adsorption efficiency in an aqueous solution. Modeling phenol and nitrophenols adsorption onto activated carbon is very complex, it requires the solution of systems of equations that involve the radiant energy balance, the spatial distribution of the adsorbed radiation, mass transfer and the mechanisms of the adsorption transport problem (Altenor et al., 2009, Kumar and Jena, 2016). The studied adsorption process involves forces near the surface of the molecules, as well as the attractive and repulsive mobility forces between molecules of various types and shapes, which include the effects of molecular dissymmetry on properties of matter, such as evaporation, condensation and reflection. The process depends on several factors and phenomena, exhibiting a nonlinear behavior which is difficult to describe by linear mathematical models, such as those derived by linear regression methods. In similar problems, these considerations have lead researchers to turn towards non-linear data driven modeling methods (Nia et al., 2014, Karimi and Ghaedi, 2014, Dil et al., 2015, Ghaedi et al., 2015a, Ghaedi et al., 2015b).

Appropriate process models could be used to take corrective actions when the adsorption efficiency is predicted to be outside the required levels. The ability to take such actions could help reduce the environmental impact of the effluents when they are discharged into the water stream.

Therefore, the first main contribution of this work is the proposal of a relatively simple data-driven approach to derive models of the adsorption efficiency, based on a real-world experimental study. The derived models can help researchers and practitioners predict the effect that variations in the operating conditions might have on the adsorption process, without recurring to costly and time-consuming experimental tests. The second contribution of this paper relates to the specific algorithmic approach taken in this work, which is based on the use of genetic programming (GP) (Koza, 1992). Basically, we pose a supervised learning problem to automatically derive descriptive models of the adsorption efficiency and solve the problem by means of GP.

From the field of evolutionary computation, GP is similar to other machine learning techniques such as neural networks, but requires less a priori knowledge of the solution structure (Langdon and Poli, 2002, Poli et al., 2008, Garg and Lam, 2015). In this work, we compare two state-of-the-art GP methods recently proposed by the authors: (1) GP with local search optimization (GP-LS); and (2) neat-GP, a variant that searches for parsimonious solutions. The GP algorithms are also compared with standard regression techniques, including multivariate linear, quadratic and robust regression. Moreover, GP is also compared with more powerful regression techniques, namely Multivariate Adaptive Regression Splines (Friedman, 1991) and the Fast Function Extraction algorithm (McConaghy et al., 2011). We present a comprehensive numerical and statistical comparison of the results, showing that GP can be used to construct useful predictive models of the phenol adsorption efficiency by activated carbon, outperforming all other methods.

The remainder of this paper is organized as follows. Section 2 provides a detailed description of the process of adsorption of phenol and nitrophenols by activated carbon. Afterward, section 3 provides an overview of GP and describes our GP-based modeling algorithms, neat-GP and GP-LS. Section 4 presents our experimental work, describing how we derive a representative dataset of the adsorption process, the experimental setup and a discussion of the main results. Finally, Section 5 presents our conclusions and outlines future work.

Section snippets

Phenol and nitrophenol adsorption by activated carbon

The process of adsorption refers to the adhesion of molecules from a mixture in a gaseous or liquid state onto a solid surface. This process creates a film of the adsorbate on the surface of the adsorbent. The present work focuses on activated carbon as an absorbent, which has been widely used in industry for many years, due to its uniform porous structure and appropriate selective adsorptive (Ruthven, 1984, Speight, 1991).

The surface properties of activated carbon used in this work are

Genetic programming

GP is part of the larger research area known as evolutionary computation, which deals with the development of global search and optimization algorithms that are based on biological evolutionary theory (Koza, 1992, Langdon and Poli, 2002, Poli et al., 2008, Garg and Lam, 2015), distinguishing itself from other evolutionary algorithms (EAs) in several ways. GP searches for syntactic expressions that perform some form of computation, starting from a pool of solution candidates. The goal is to find

Experiments

As stated before, the goal of this work is to derive a a model that describes the relationship between operating variables and adsorption efficiency of phenol and nitrophenols onto activated carbon. In particular, we use a data-driven approach, where real experimental data is generated, recorded and then used to pose a supervised-learning problem. Afterward, GP (GP-LS and neat-GP) is used to solve a supervised learning problem. Performance is compared with several baseline and state-of-the art

Summary, conclusions and future work

The effective planning and management of industrial process that release contaminants into our ecosystem has become a primary concern over recent decades, since contamination and resource scarcity problems have led to a variety of impacts on our well being. However, achieving a reasonable and efficient management strategy is difficult since many conflicting factors have to be balanced, due to complexities of real world problems.

This paper presents the first application of GP to model the

Acknowledgments

This research was partially supported by CONACYT (México) Basic Science Research Projects no.169133 and no.178323, CONACYT Fronteras de la Ciencia 2015-2 Project No. 944, TecNM (México) Research Projects no. 5636.15-P and no. 5621.15-P, as well as by FP7-Marie Curie-IRSES 2013 European Commission program with project ACoBSEC with contract no. 612689. First and fifth authors are supported by CONACYT graduate scholarships, respectively no. 294213 and no. 332554.

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