Design of bio-oil additives via molecular signature descriptors using a multi-stage computer-aided molecular design framework

doi:10.1007/s11705-021-2056-8

Front. Chem. Sci. Eng.

2022, Vol. 16

Issue (2) : 168-182 https://doi.org/10.1007/s11705-021-2056-8

RESEARCH ARTICLE

Design of bio-oil additives via molecular signature descriptors using a multi-stage computer-aided molecular design framework

Jia Wen Chong¹, Suchithra Thangalazhy-Gopakumar¹, Kasturi Muthoosamy², Nishanth G. Chemmangattuvalappil¹(

)

¹. Department of Chemical and Environmental Engineering, University of Nottingham Malaysia, Selangor 43500, Malaysia
². Nanotechnology Research Group, Centre of Nanotechnology and Advanced Materials, University of Nottingham Malaysia, Selangor 43500, Malaysia

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Abstract

Direct application of bio-oil from fast pyrolysis as a fuel has remained a challenge due to its undesirable attributes such as low heating value, high viscosity, high corrosiveness and storage instability. Solvent addition is a simple method for circumventing these disadvantages to allow further processing and storage. In this work, computer-aided molecular design tools were developed to design optimal solvents to upgrade bio-oil whilst having low environmental impact. Firstly, target solvent requirements were translated into measurable physical properties. As different property prediction models consist different levels of structural information, molecular signature descriptor was used as a common platform to formulate the design problem. Because of the differences in the required structural information of different property prediction models, signatures of different heights were needed in formulating the design problem. Due to the combinatorial nature of higher-order signatures, the complexity of a computer-aided molecular design problem increases with the height of signatures. Thus, a multi-stage framework was developed by developing consistency rules that restrict the number of higher-order signatures. Finally, phase stability analysis was conducted to evaluate the stability of the solvent-oil blend. As a result, optimal solvents that improve the solvent-oil blend properties while displaying low environmental impact were identified.

Keywords computer-aided molecular design bio-oil additives molecular signature descriptor

Corresponding Author(s): Nishanth G. Chemmangattuvalappil

Just Accepted Date: 08 May 2021 Online First Date: 17 June 2021 Issue Date: 10 January 2022

Cite this article:

Jia Wen Chong,Suchithra Thangalazhy-Gopakumar,Kasturi Muthoosamy, et al. Design of bio-oil additives via molecular signature descriptors using a multi-stage computer-aided molecular design framework[J]. Front. Chem. Sci. Eng., 2022, 16(2): 168-182.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-021-2056-8
https://academic.hep.com.cn/fcse/EN/Y2022/V16/I2/168

Fig.1 Framework for the development of CAMD model for the design of solvent.

Tab.1 Free bond groups in terms of signature of height 2

Tab.2 Allowed combination of groups

Rule	Structural constraint	Equation
I	$∑ i = 1 n 1 x i + 2 ∑ n 1 n 2 x i + 3 ∑ n 2 n 3 x i + 4 ∑ n 3 n 4 x i = 2 [(∑ i = 1 N x i + 12 ∑ i = 0 N D i x i + ∑ i = 0 N M i x i + ∑ i = 1 N T i x i) − 1]$	(13)
II	$∑ ? (l i → l j) h = ∑ ? (l j → l i) h$	(14)

Tab.3 Mathematical expression for structural constraints

Tab.4 Set of signatures for 2-octanol with its corresponding height 2 signatures

Requirement/need	Targeted property	Constraint
Liquid state at room temperature	Normal boiling point/K	>400.15
Liquid state at room temperature	Normal melting point/K	<298.15
Fuel combustion quality	Higher heating value	To be maximised
Fuel flow consistency	Viscosity/(mPa·s)	1> $ν$ >6
Fuel flow consistency	Density/(kg·m^–3)	800> $ρ$ >1000
Homogenous form	Tangent plane distance	To be determined
Environmental related properties and toxicology	Aquatic acute toxicity, LC₅₀	>100
	Aquatic acute toxicity, EC₅₀	>100
	Oral acute toxicity, LD₅₀	>100
	Bioconcentration factor	<1000
	Soil-water partition coefficient/(L·kg^–1)	<31622
	Global warming potential	<10
	Photochemical oxidation potential	<10

Tab.5 Translation of product requirements into target properties and constraints

Tab.6 Example of 2nd order group expressed in terms of signature of height 2 or 3

Fig.2 Generation of height 2 signature based on the height 1 signature, CI(C).

Tab.7 Potential height 1, 2, 3 and 4 signatures generated

Tab.8 Higher heating values obtained from NIST’s database and present work for respective solvent candidates

Fig.3 Gibbs energy and tangent plot for 2-octanol and bio-oil (a) 16% water content, (b) 25% water content, and (c) 40% water content.

Fig.4 Gibbs phase ternary graph of bio-oil, water and 2-octanol.

Tab.9 The identified feasible solvent candidates

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