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Articles

Synthesis of biodiesel via pre-blending of feedstocks: an optimization by the polynomial curve fitting method

ORCID Icon, ORCID Icon, , ORCID Icon, & ORCID Icon
Pages 679-688 | Received 14 May 2018, Accepted 28 Jul 2018, Published online: 20 Nov 2018
 

Abstract

Use of abundant indigenous energy crops for biofuel production contributes to enhance the economy and reduce the dependence on fossil fuels. This article aims to shed light on two energy crops available in Pakistan; castor (Ricinus communis L.) and cottonseed (Gossypium hirsutum L.). Although castor has excellent oil content (54%), its high viscosity is not acceptable. Therefore, pre-blending with cottonseed oil was proposed. Physical and chemical properties of crude oils, pure biodiesels and binary blends besides fatty acid composition and degree of unsaturation were analyzed. Biodiesels were checked for quality parameters within limits of American Society for Testing Materials (ASTM) and European Standards. Improvement in kinematic viscosity and density of castor biodiesel were noticed following this concept. Moreover, the developed empirical formula indicated that an optimized blending ratio of 93.86% cottonseed oil and 6.14% castor oil accomplished biodiesel yield of 91.12%, kinematic viscosity of 6 mm2/s and cetane number of 48.79, respectively, which all satisfy ASTM D6751. This technique also indicated that pre-blending can raise the ester content of castor biodiesel. In conclusion, it is recommended to adopt the concept of pre-blending to improve the quality of biodiesel and thus the engine and emission performance in compression ignition engines.

Novelty statement

Valorization of indigenous energy crops is gaining increasing attention to reduce the dependence of many countries such as Pakistan on imported fossil fuels. Pre-blending of different feedstocks can be envisioned as a remedy to improve some biodiesel properties. Castor biodiesel has a high kinematic viscosity that does not satisfy international standards. Pre-blending with cottonseed oil at various percentages followed by finding an optimized blend using a polynomial curve fitting technique is one effective way to reduce the high viscosity of castor biodiesel and yield a biodiesel that satisfies the ASTM D6751 limit. Adopting such technique helps the country to produce biodiesel from castor oil with optimized properties. This helps policymakers and the market to produce biodiesel from various feedstocks. It also has a positive impact on engine and emissions performance in CI engines. Economic benefits are also anticipated as it can help diversify the options for biodiesel production rather than depending on single feedstock. FT-IR analysis, which was adopted as a simple and rapid technique, can serve as a model to study the structure of biodiesel produced from pre-blended oils without the need for sample preparation.

Figure 1. FAME yield of pure and blended oils. Note: Blend 1 (75% cotton, 25% castor), Blend 2 (50% cotton, 50% castor), and Blend 3 (25% cotton, 75% castor).

Figure 1. FAME yield of pure and blended oils. Note: Blend 1 (75% cotton, 25% castor), Blend 2 (50% cotton, 50% castor), and Blend 3 (25% cotton, 75% castor).

Figure 2. FT-IR spectra of Castor B100, Cotton B100, Blend 1, Blend 2 and Blend 3. Note: Blend 1 (75% cotton, 25% castor), Blend 2 (50% cotton, 50% castor), and Blend 3 (25% cotton, 75% castor).

Figure 2. FT-IR spectra of Castor B100, Cotton B100, Blend 1, Blend 2 and Blend 3. Note: Blend 1 (75% cotton, 25% castor), Blend 2 (50% cotton, 50% castor), and Blend 3 (25% cotton, 75% castor).

Figure 3. FT-IR spectra of pure castor oil, cottonseed oil, Blend 1, Blend 2 and Blend 3. Note: Blend 1 (75% cotton, 25% castor), Blend 2 (50% cotton, 50% castor), and Blend 3 (25% cotton, 75% castor).

Figure 3. FT-IR spectra of pure castor oil, cottonseed oil, Blend 1, Blend 2 and Blend 3. Note: Blend 1 (75% cotton, 25% castor), Blend 2 (50% cotton, 50% castor), and Blend 3 (25% cotton, 75% castor).

Figure 4. Prediction of blends properties (a) Kinematic viscosity (b) Density (c) Cetane number.

Figure 4. Prediction of blends properties (a) Kinematic viscosity (b) Density (c) Cetane number.

Disclosure statement

No potential conflict of interest was reported by the authors.

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