PREPARATION AND CHARACTERIZATION OF WATER-IN-OIL (W/O) NANO-EMULSIONS

Abstract

The growing concern on green technology to protect the environment has encouraged the use of alternative ecofriendly formulations for nano-emulsion fuels. Nowadays, diesel engine is the most powerful and widely used internal combustion engine; its exhaust, owing to incomplete combustion, releases particulate matters (unburnt hydrocarbons) and detrimental gases like NOX, CO, and CO2 to the environment. It has been reported that the addition of water to diesel fuel brings significant reductions in the emission of unburnt hydrocarbons and NOX gases due to reduced temperature in combustion chamber. Improved combustion properties like brake specific fuel consumption and increased brake thermal efficiency has been achieved because of ignition delay and micro-explosion. In case of water-in-oil (w/o NE) nano-emulsion fuel, smaller size droplets increase surface area that helps in efficient combustion improving brake specific fuel consumption (BSFC) and reducing combustion chamber temperature consequently helping in further reduction in NOx and PMs. Moreover, their smaller size negates gravity induced separation and they make the balance between Brownian motion and surface induced properties making them stable for longer storage time. Present work therefore, motivates us for the development of transparent and stable w/o nano-emulsions. A detailed study regarding formation of water-in-diesel oil is required by applying low and high energy methods. In this regard a detailed ternary diagram is required to study the instability mechanism and rheological analysis for the system being developed. Moreover, a strategic modeling approach is required to optimize the process parameters for the formation of o/w NEs. The overall objectives of the proposed work were the formation and characterization of nano-emulsion fuel. Formation of water-in-diesel oil (w/o NE) nano-emulsion has been achieved by low energy emulsification method by stabilizing a new combination of non-ionic sorbitan esters surfactants, i.e. PEG20-sorbitan mono-stearate and sorbitan mono-oleate in mixed proportions. Different combinations of surfactants (T6+S8) have been tested and best possible combination of mixed surfactants is found at a surfactants ratio of 35:65 (wt. /wt.) for T6:S8 at HLB = 8.01, which resulted into smaller droplet size of 44.87 nm. A phase diagram study is performed to identify the zones of formation of transparent, translucent and opaque emulsions (44 nm<droplet size<700nm) at 37 oC. Mechanism responsible for instability of emulsion is explained by Ostwald ripening with inference describing a decrease in particle size with Ostwald ripening rate. In case of nano-emulsion of droplet size 64.28 nm the Oswald ripening rate is found as 0.0874x10-27 m3s-1. Comparison of Ostwald ripening rate with other set of surfactants obtained iv by different authors showed the lowest rate among them indicative of enhanced stability. A rheological study of tested set of nano-emulsions depicted Newtonian behavior (1.0371 ≤ n ≤ 1.0826) over a wider range of shear rate (10 - 1000 s-1) at different temperatures (25 - 40 oC). An energy efficient and scalable method is designed to form stable and transparent water-in-oil (w/o) nano-emulsion. Application of high energy in addition to the low energy at the optimized conditions have been targeted to make the process energy efficient, since later part is applied to droplets formed at less energy. In the present work, formation of combined energy mixed surfactant nano-emulsion is achieved by combined approach of isothermal low energy followed by high energy method (ultrasonication). A mixture of two functional groups (ether and ester) non-ionic surfactants is used at optimized ratio of 0.71/0.29 (Span 80/Tx-100; w/w). Optimization of ultrasonicated parameters resulted in 25% amplitude, 0.5 pulse mode factor and 8.5 minutes of sonication time. A ternary diagram study has been performed to recognize the compositions accountable for the formation of transparent, translucent and opaque emulsions in the bounded range of water fraction 0.02 to 0.11 and surfactant fraction 0.10 to 0.20. Surfactant-to-water (β) ratio found applicable for the production of nano-sized droplets in the range of 2β3. A minimum droplet size of 251 nm is attained in the present study. An increase in surfactant fraction decreased average droplet size, whereas, increase in water fraction increased average droplet size. Reduction in droplet size is prominently found in the range of energy density from 15.23 J.ml-1 to 40 J.ml-1 thereafter, it decelerated up to 160 J.ml-1. Prediction of average droplet size modeled with energy density fitted well and could be used for scaling up and tuning the droplet size. Resultant nano-emulsion samples displayed kinetic stability whereas long term stability (45 days) assessed using Ostwald ripening model showed stability in the order of β=2.0>β=2.5>β=3.0>β=4.0. Furthermore, statistical and mathematical approach has been implemented in finding out optimal values of process parameters that could formulate water emulsified diesel fuels with lower droplet size (Dz-avg.) of dispersed phase and at the same time possesses lower values of kinematic viscosity. In order to impart optimal properties in the diesel, an integral hybrid genetic algorithm (GA) has been implemented with back propagation artificial neural network (BPANN) and response surface methodology (RSM) based on rotatable central composite design (RCCD). Process parameters as input to the proposed models were water fraction (0.05-0.11, w/w), surfactant fraction (0.10-0.020, w/w), power density (21.25-46.75, W.cm-2), and ultrasonication time (4-10, min.). Response variables like Dz-avg. (nm) and kinematic viscosity (mm2.s-1) are employed as output variables. In the anticipated hybrid GA model, a multi objective optimization v is performed wherein, model equations obtained from RCCD-RSM are used as fitness function and output from RSM-BPANN are used as initial population. Hybrid GA model predicted optimum values of Dz-avg.. and kinematic viscosity as 53.54 nm and 1.459 mm2.s-1, respectively, with percent errors as 2.17% and 0.34 % respectively. However, optimized process parameters has been predicted as water fraction-0.052 (w/w), surfactant fraction-0.105 (w/w), power density-29.94 (W.cm-2), and ultrasonication time-9.7 minutes. Hybrid GA model proposed in this study is found effective in predicting the optimized values of process variables in the formation of nano-emulsion diesel fuel with optimized values of avg. droplet size and kinematic viscosity.