Abstract

Atmospheric concentrations of carbon dioxide (CO2) are significantly increasing since the industrial revolution at an accelerating rate causing environmental impact such as global warming and climate change. Projections indicate that CO2 concentrations will continue to rise to unsustainable levels. This highlights the scale of the challenge our scientists are facing in order to reduce CO2 emissions and underpins the importance of promoting green process engineering for the utilisation of CO2 as a valuable commodity in the process industry. The transformation of CO2 to value-added chemicals such as organic carbonates provides a promising technological advancement aimed at reducing CO2 atmospheric concentrations to sustainable levels. Dimethyl carbonate (DMC) is a promising green compound that exhibits versatile and excellent chemical properties and therefore finds applications as an intermediate in the chemical and pharmaceutical industries. DMC has a high oxygen content and can be used as an oxygenate additive to gasoline to improve its performance and reduce exhaust emission. The conventional method for DMC synthesis involves the utilisation of phosgene as a toxic feedstock. Thus, greener and more sustainable synthetic processes for the synthesis of DMC are required. Recently, non-toxic synthetic routes have been explored; these include, oxidative carbonylation of carbon monoxide (CO), oxygen (O2) and MeOH, direct synthesis from MeOH and CO2 and the transesterification of cyclic carbonates and methanol (MeOH). The oxidative carbonylation route suffers from the use of expensive raw materials and corrosive reagents as well as being hazardous due to the explosive potential of CO. The direct production of DMC from MeOH and CO2 offers an attractive and green synthetic route for DMC synthesis. Also, the synthesis of DMC via the transesterification of cyclic carbonates and MeOH, where cyclic carbonates can be synthesised from their corresponding epoxides and CO2, makes the synthesis of DMC via transesterification route more environmentally friendly and desirable in terms of green chemistry and sustainable development. Therefore, in this research new greener catalytic processes for DMC synthesis via addition of MeOH to CO2 route and transesterification route have been explored. In this work, several commercially available heterogeneous catalysts such as ceria and lanthana doped zirconia (Ce–La–Zr–O), ceria doped zirconia (Ce–Zr– O), lanthana doped zirconia (La–Zr–O), lanthanum oxide (La–O) and zirconium oxide (Zr–O) have been extensively assessed for the synthesis of DMC. Strongly coupled graphene based inorganic nanocomposites represent an exciting and new class of functional materials and therefore the utilisation of graphene oxide (GO) as a suitable support for metal oxide catalysts has been explored. Ceria doped zirconia graphene nanocomposites (Ce–Zr/GO) have been synthesised using conventional wet impregnation methods. Samples of Ce–Zr/GO have been subjected to heat treatment at various temperatures (773 K, 873 K, 973 K and 1073 K) in an attempt to enhance their catalytic performance. As-prepared Ce– Zr/GO sample and the corresponding heat treated samples have been assessed for the direct synthesis for DMC from MeOH and CO2. Furthermore, a new innovative approach has been employed for synthesising advanced, highly efficient and active heterogeneous catalysts via utilisation of a continuous hydrothermal flow synthesis (CHFS) reactor. Tin doped zirconium oxide (Zr–Sn– O) and tin doped zirconia/graphene nanocomposite (Zr–Sn/GO) have been assessed as suitable heterogeneous catalysts for the synthesis of DMC via the transesterification route. The catalysts were characterised using various analytical techniques such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and Brunauer-Emmett-Teller (BET) surface area measurement. A heterogeneous catalytic process for the synthesis of DMC has been investigated using a high pressure reactor. The effect of various reaction parameters such as the reactant molar ratio, catalyst loading, reaction temperature, CO2 pressure, reaction time and the use of a dehydrating agent was studied for the optimisation of DMC synthesis. Reusability studies were conducted to evaluate the long term stability of the heterogeneous catalysts by recycling and reusing the catalyst several times for the synthesis of DMC. Tin doped zirconia graphene oxide (Sn–Zr/GO) nanocomposite catalyst has been found to be the best performed catalyst for the synthesis of DMC as compared to other catalysts evaluated in this research work. This can be attributed to the phase composition and crystallinity of the catalyst along with the defects on the graphene sheet such as, holes, acid/basic groups and presence of residual which can provide additional active catalytic sites. Catalyst reusability studies evidently showed that Sn–Zr/GO nanocomposite can be easily recovered and reused without any significant reduction in the catalytic performance. Response Surface Methodology (RSM) has track record in helping researchers in modeling and optimisation of the experimental design for various applications in food industry, catalysis and chemical reaction engineering. Therefore, it has been employed to evaluate the relationship between multiple process variables in order to optimise a specified response (i.e. yield of DMC). RSM using Box-Behneken design (BBD) was carried out for process modeling and optimisation, with an aim to better understand the relationship between five operating variables (i.e. MeOH:PC molar ratio, catalyst loading (w/w), reaction temperature, reaction time and stirring speed) and their impact on the yield of DMC. A model for the synthesis of DMC by transesterification of PC and MeOH has been developed using BBD to compare the experimental data and the predicted results by the BBD model. Furthermore, regression analysis was applied to establish the optimum reaction conditions for a maximising DMC synthesis. The BBD model predicted values are in good agreement with the experimental results.

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