Optimal Descriptor Based QSPR Models for Catalytic Activity of Propylene Polymerization

Optimal Descriptor Based QSPR Models for Catalytic Activity of Propylene Polymerization

Sanija Begum (Department of Chemistry, Siksha ‘O'Anusandhan Deemed University, Bhubaneswar, India) and P. Ganga Raju Achary (Department of Chemistry, Siksha ‘O'Anusandhan Deemed University, Bhubaneswar, India)
DOI: 10.4018/IJQSPR.2018070103

Abstract

A heterogeneous Ziegler–Natta (ZN) catalyst is an important catalyst in the field of the polypropylene polymerization industry. The role of electron donors has been crucial in the ZN catalyzed polypropylene polymerization process. In this article, quasi-SMILES-based QSPR models are elaborated for the prediction of catalytic activities. The representations of the molecular structure by quasi-simplified molecular input line entry system were the basis to build the desired QSPR model. These models were developed by means of the Monte Carlo optimization involving the available methods classic scheme (CS), balance of correlations (BC) and balance of correlation with ideal slopes (BCIS). The best QSPR model showed r2 = 0.813 (for external validation set), rm2 (avg)=0.73 and ∆rm2= 0.03.
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1. Introduction

Ziegler-Natta polymerization is an important method for vinyl polymerization, as it provides control to prepare the polymers of specific tacticity. The preparation of the linear unbranched polyethylene and isotactic polypropylene is only possible by Ziegler-Natta catalysts. Today in the manufacture of polypropylene (PP), most of the commercial catalysts are obtained by modifying the parent Ziegler – Natta system (Ratanasak, Rungrotmongkol, Saengsawang, Hannongbua, & Parasuk, 2015; Taniike & Terano, 2012). These catalysts comprise the MgCl2-supported TiCl4 catalyst in conjunction with triethylaluminium [Al(C2H5)3] as co-catalyst and organic additives (electron donors) for the production of isotactic polypropylene. These electron donors can be internal donors (which are added during the catalyst preparation) or external donors for the propylene polymerization process (Ratanasak et al., 2015). Internal electron donors are bonded directly to the MgCl2 support and activate the site formation whereas the external electron donor could selectively poison these sites (Albizzati et al., 1995; Xu, Feng, & Yang, 1997; Makwana et al., 2009; Matyjaszewski, Gaynor, Greszta, Mardare, & Shigemoto, 1995; Sacchi, Tritto, & Locatelli, 1991). Therefore, the direct relation of these internal donors such as phthalates, 1,3-diethers and malonates present in ZN catalyst with the production of the polypropylene is obvious and interesting. The polypropylene activity (PPact) can be defined as the how many kilograms of PP are obtained per gram of the ZN catalyst with 29 internal electron donors: Phthalates, 1, 3-diethers and Malonates. The PPact is expressed in kg PP/g Cat. Earlier attempts have been made to correlate PPact with adsorption energy (Ratanasak et al., 2015); however, the correlation between the PPact and the internal donors of ZN catalyst is so far not reported in detail.

We know that the quantitative ̶ structure activity/property relationship (QSAR/QSPR) is a proven technique to predict the desired activities/properties from the molecular properties. The QSAR/QSPR is also successfully applied for both homogenous (Cruz et al., 2004; Cruz, Martinez, Martinez-Salazar, Polo-Cerón, et al., 2007; Cruz, Martinez, Martinez-Salazar, Ramos, et al., 2007; Martínez, Cruz, Ramos, & Martínez-Salazar, 2012; Yao, Shoji, Iwamoto, & Kamei, 1999) and heterogeneous catalysts (Cruz, Martinez, Martinez-Salazar, Polo-Cerón, et al., 2007; Fayet, Raybaud, Toulhoat, & de Bruin, 2009; Tognetti, Fayet, & Adamo, 2010). This field helps in designing materials, screening of potential catalysts before the material being synthesised minimizing the cost and sometimes can suggest the mechanism (Boudene, De Bruin, Toulhoat, & Raybaud, 2012; Colosi, Huang, & Weber, 2010; di Lena et al., 2010; Manz et al., 2012; Taniike & Terano, 2012; Wu et al., 2012). Also, good QSAR models could be built by known paradigms such as “Endpoint=f (SMILES), (Achary, 2014; Begum & Achary, 2015), Endpoint = f (quasi-SMILES)”. The quasi-SMIES, the representation of eclectic data, can be symbols of the SMILES structure/or groups in the molecular structure. (Toropov et al., 2013; Toropov & Toropova, 2014; Toropova & Toropov, 2013).

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