Identification and Quantification of Hydrocarbon Functional Groups in Gasoline Using 1H-NMR Spectroscopy for Property Prediction

Gasoline is one of the most important distillate fuels obtained from crude refining; it is mainly used as an automotive fuel to propel spark-ignited (SI) engines. It is a complex hydrocarbon fuel that is known to possess several hundred individual molecules of varying sizes and chemical classes. These large numbers of individual molecules can be assembled into a finite set of molecular moieties or functional groups that can independently represent the chemical composition. Identification and quantification of groups enables the prediction of many fuel properties that otherwise may be difficult and expensive to measure experimentally. In the present work, high resolution 1H nuclear magnetic resonance (NMR) spectroscopy, an advanced structure elucidation technique, was employed for the molecular characterization of a gasoline sample in order to analyze the functional groups. The chemical composition of the gasoline sample was then expressed using six hydrocarbon functional groups, as follows: paraffinic groups (CH, CH2 and CH3), naphthenic CH-CH2 groups and aromatic C-CH groups. The obtained functional groups were then used to predict a number of fuel properties, including research octane number (RON), motor octane number (MON), derived cetane number (DCN), threshold sooting index (TSI) and yield sooting index (YSI)

Predicting physical properties of oxygenated gasoline and diesel range fuels using machine learning

Understanding the physical properties of distillate petroleum fuels like gasoline and diesel is very critical to ensure the normal operation of internal combustion (IC) engines with regards to processes like spray atomization, heating, evaporation etc. Two of most important physical properties are density and viscosity. Many factors such as molecular structure, molecular weight, temperature etc. effect the physical properties of the fuel. The present work deals with the development of a machine learning model for predicting the density and viscosity of petroleum fuels containing oxygenated chemical classes such as alcohols, esters, ketones and aldehydes. The model was developed using the molecular structure of the compounds expressed in the form of functional groups as inputs. The density and viscosity of 164 pure compounds spanning various chemical families and 14 blends of known compositions was collected from the literature. An artificial neural network model (ANN) for predicting density and viscosity was developed using the neural network tool in Matlab. Each of the ANN model was tested against 15% of the data and the results show that the models were able to successfully predict the density and viscosity of the unseen data points to a good accuracy. A regression coefficient of 0.99 (for density) and 0.98 (for viscosity) was obtained for the test set. The developed models can be used to predict and screen the density and viscosity of real petroleum fuels containing drop in oxygenated bio-fuels

Simulation and Modelling of Hydrogen Production from Waste Plastics: Technoeconomic Analysis

The global energy demand is expected to increase by 30% within the next two decades. Plastic thermochemical recycling is a potential alternative to meet this tremendous demand because of its availability and high heating value. Polypropylene (PP) and polyethylene (PE) are considered in this study because of their substantial worldwide availability in the category of plastic wastes. Two cases were modeled to produce hydrogen from the waste plastics using Aspen Plus®. Case 1 is the base design containing three main processes (plastic gasification, syngas conversion, and acid gas removal), where the results were validated with the literature. On the other hand, case 2 integrates the plastic gasification with steam methane reforming (SMR) to enhance the overall hydrogen production. The two cases were then analyzed in terms of syngas heating values, hydrogen production rates, energy efficiency, greenhouse gas emissions, and process economics. The results reveal that case 2 produces 5.6% more hydrogen than case 1. The overall process efficiency was enhanced by 4.13%. Case 2 reduces the CO2 specific emissions by 4.0% and lowers the hydrogen production cost by 29%. This substantial reduction in the H2 production cost confirms the dominance of the integrated model over the standalone plastic gasification model

Predicting Fuel Ignition Quality Using ¹H NMR Spectroscopy and Multiple Linear Regression

An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using ¹H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH₃ groups, paraffinic CH₂ groups, paraffinic CH groups, olefinic CH–CH₂ groups, naphthenic CH–CH₂ groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using ¹H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (<i>R</i>² = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data

Utilization of Low-Rank Coals for Producing Syngas to Meet the Future Energy Needs: Technical and Economic Analysis

Increased energy demand in recent decades has resulted in both an energy crisis and carbon emissions. As a result, the development of cleaner fuels has been under the research spotlight. Low-rank coals are geographically dispersed, abundant, and cheap but are not utilized in conventional processes. Syngas can be produced from coal-using gasification which can be used in various chemical engineering applications. In this study, the process model for syngas production from low-rank coal is developed and the effects of various process parameters on syngas composition are evaluated, followed by a technical and economic evaluation. The syngas production rate for the low-rank coal has been evaluated as 25.5 kg/s, and the contribution to H2 and CO production is estimated as 1.59 kg/s and 23.93 kg/s, respectively. The overall syngas production and energy consumed in the process was evaluated as 27.68 kg/GJ, and the CO2 specific emissions were calculated as 0.20 (mol basis) for each unit of syngas production. The results revealed that the syngas production efficiency for low-rank coals can be as high as 50.86%. Furthermore, the economic analysis revealed that the investment and minimum selling prices per tonne of syngas production are EUR 163.92 and EUR 180.31, respectively

Technoeconomic Feasibility of Hydrogen Production from Waste Tires with the Control of CO2Emissions

The worldwide demand for energy is increasing significantly, and the landfill disposal of waste tires and their stockpiles contributes to huge environmental impacts. Thermochemical recycling of waste tires to produce energy and fuels is an attractive option for reducing waste with the added benefit of meeting energy needs. Hydrogen is a clean fuel that could be produced via the gasification of waste tires followed by syngas processing. In this study, two process models were developed to evaluate the hydrogen production potential from waste tires. Case 1 involves three main processes: The steam gasification of waste tires, water gas shift, and acid gas removal to produce hydrogen. On the other hand, case 2 represents the integration of the waste tire gasification system with the natural gas reforming unit, where the energy from the gasifier-derived syngas can provide sufficient heat to the steam methane reforming (SMR) unit. Both models were also analyzed in terms of syngas compositions, H2production rate, H2purity, overall process efficiency, CO2emissions, and H2production cost. The results revealed that case 2 produced syngas with a 55% higher heating value, 28% higher H2production, 7% higher H2purity, and 26% lower CO2emissions as compared to case 1. The results showed that case 2 offers 10.4% higher process efficiency and 28.5% lower H2production costs as compared to case 1. Additionally, the second case has 26% lower CO2-specific emissions than the first, which significantly enhances the process performance in terms of environmental aspects. Overall, the case 2 design has been found to be more efficient and cost-effective compared to the base case design

Technoeconomic Feasibility of Hydrogen Production from Waste Tires with the Control of CO2Emissions

The worldwide demand for energy is increasing significantly, and the landfill disposal of waste tires and their stockpiles contributes to huge environmental impacts. Thermochemical recycling of waste tires to produce energy and fuels is an attractive option for reducing waste with the added benefit of meeting energy needs. Hydrogen is a clean fuel that could be produced via the gasification of waste tires followed by syngas processing. In this study, two process models were developed to evaluate the hydrogen production potential from waste tires. Case 1 involves three main processes: The steam gasification of waste tires, water gas shift, and acid gas removal to produce hydrogen. On the other hand, case 2 represents the integration of the waste tire gasification system with the natural gas reforming unit, where the energy from the gasifier-derived syngas can provide sufficient heat to the steam methane reforming (SMR) unit. Both models were also analyzed in terms of syngas compositions, H2production rate, H2purity, overall process efficiency, CO2emissions, and H2production cost. The results revealed that case 2 produced syngas with a 55% higher heating value, 28% higher H2production, 7% higher H2purity, and 26% lower CO2emissions as compared to case 1. The results showed that case 2 offers 10.4% higher process efficiency and 28.5% lower H2production costs as compared to case 1. Additionally, the second case has 26% lower CO2-specific emissions than the first, which significantly enhances the process performance in terms of environmental aspects. Overall, the case 2 design has been found to be more efficient and cost-effective compared to the base case design

Process design and techno-economic analysis of dual hydrogen and methanol production from plastics using energy integrated system

This study has been dedicated towards the conversion of plastics to methanol and hydrogen. The base design (case 1) represents the conventional design for producing syngas via steam gasification of waste plastics followed by CO2 and H₂S removal. The syngas then processed in the methanol synthesis reactor to produce methanol, whereas, the remaining unconverted gases are processed in water gas shift reactors to produce hydrogen. On the other hand, an alternative design (case 2) has been also developed with an aim to increase the H2 and methanol production, which integrates the plastic gasification and the methane reforming units to utilize the high energy stream from gasification unit to heat up the feed stream of reforming unit. Both the cases have been techno-economically compared to evaluate the process feasibility. The comparative analysis revealed that case 2 outperforms the case 1 in terms of both process efficiency and economics