Ramaraj Rameshprabu

Bio: Ramaraj Rameshprabu is an academic researcher from Maejo University. The author has contributed to research in topics: Biofuel & Alternative energy. The author has an hindex of 2, co-authored 2 publications receiving 45 citations.

Biodiesel from Green Alga Scenedesmus acuminatus

Abstract: Renewable fuels for alternative energy sources have been paid a great attention in recent years. Biodiesel has been gaining worldwide popularity as an alternative energy source. The production of biofuels from microalgae, especially biodiesel, has gained huge popularity in the recent years, and it is assumed that, due to its eco-friendly and renewable nature, it can replace the need of fossil fuels. Scenedesmus genus was discussed by phycologists as promising microalgae for biofuel production based on its biomass and fatty acid productivity. In the present study, S. acuminatus was cultivated in piggery wastewater effluent to couple waste treatment with biodiesel production. The batch feeding operation by replacing 10% of algae culture with Piggery wastewater effluent every day could provide a stable net biomass productivity of 3.24 g L−1 day−1. The effect of acid hydrolysis of lipids from S. acuminatus on FAME (fatty acid methyl esters) production was investigated. Direct transesterification (a one-stage process) of the as harvested S. acuminatus biomass resulted in a higher bio-diesel yield content than that in a two-stage process. This study results revealed that it is feasible to produce biodiesel from wet microalgae biomass directly without the steps of drying and lipid extraction.

A Newly Isolated Green Alga, Pediastrum Duplex Meyen, from Thailand with Efficient Hydrogen Production

Abstract: Biofuels are gaining attention worldwide as a way to reduce the dependence on fossil fuels. Biological Hydrogen (H2) production is considered the most environmentally friendly route of producing H2, fulfilling the goals of recycling renewable resources and producing clean energy. It has attracted global attention because of its potential to become an inexhaustible, low cost, renewable source of clean energy and appears as an alternative fuel. H2 production processes offer a technique through which renewable energy sources like biomass can be utilized for the generation of the cleanest energy carrier for the use of mankind. This paper presents laboratory results of biological production of hydrogen by green alga was isolated from fresh water fish pond in Sansai, Chiang Mai province, Thailand. Under light microscope, this green alga was identified as belonging to the genus Pediastrum and species P. duplex Meyen. The successful culture was established and grown in poultry litter effluent medium (PLEM) under a light intensity of 37.5 μmol-1m2 sec-1 and a temperature of 25°C. The nutrient requirements and process conditions that encourage the growth of dense and healthy algal cultures were explored. The highest H2 was produced when cultivated cells in PLEM for 21 hours under light and then incubated under anaerobic adaptation for 4 hours.

Valuable bioproducts obtained from microalgal biomass and their commercial applications: A review

Abstract: Microalgae are likely to become a part of our everyday diet in the near future as they are considered to be rich in proteins, carbohydrates, and high density lipoproteins. They will play a pivotal role in the food cycle of many people around the globe. Use of microalgae in treating wastewater is also one of the disciplines which are luring researchers as this contributes to a sustainable way of exploiting resources while keeping the environment safe. In addition, microalgal biomass also has the potential to be used as a feedstock for producing biofuel, bio fertilizers, pharmaceuticals, nutraceuticals and other bio-based products. This review presents the different value-added products obtained from microalgal biomass and the applicability of these products commercially.

Bioethanol production from sunflower stalk: application of chemical and biological pretreatments by response surface methodology (RSM)

Abstract: The present paper discusses response surface methodology (RSM) as an efficient tactic for predictive model and optimization of the whole experimental methods of reducing sugar and energy. In this work, the application of RSM presented for optimizing reducing sugar and energy as compared with production between chemical and biological pretreatments. All experiments applied statistical designs in order to develop a statistic multivariate analysis model that provides to consider the effect of different parameters on a process and describe the optimum values of these variables to optimize the response. Dred sunflower stalks were pretreated by sodium hydroxide (NaOH) and Trichoderma reesei as a function of two variables: concentration of NaOH (%) and T. reesei (%) and time for pretreatment (Day) to receive reducing sugar and energy. The chemical pretreatment model was characterized by 13 runs, varying the variables at two factors, NaOH (1, 1.5, 2%) and Day (1, 2, 3). The biological pretreatment model was characterized by 13 runs, varying the variables at two factors, T. reesei (1, 1.5, 2%) and Day (1, 2, 3), by central composite design experimental design. In the chemical pretreatment, experiments performed at 2% (w/v) of NaOH for 3 days were used. The chemical pretreatment model at 2% NaOH for a 3-day release reduced sugar by 5.812 g/L and energy by 92.992 kJ/L; on the other hand, biological pretreatment model at 2% T. reesei for a 3-day release reduced sugar by 3.891 g/L and energy by 62.256 kJ/L, reducing sugar starter for fermentation by 49.0670 ± 6.4660 g/L and fermentation efficiency by 71.60% at 48 h fermented time.

ResNeXt convolution neural network topology-based deep learning model for identification and classification of Pediastrum

Abstract: For identification of different Pediastrum species in a sample, the determination of microscopic feature and colony morphology are the preliminary steps before sending them to the higher genomic and proteomic level. Great efforts with high expertise are required for the time-consuming manual process. In the present study, the first time an effort has been done to address the problem for identification and classification of Pediastrum species with the help of convolutional neural networks (CNNs). The modified ResNeXt CNN (Convolution Neural Network) model is used for training and validation of the data set consisting of 42,000 algal images. Modified ResNeXt CNN topology differentiates cells based on the formation of coenobia, cell arrangement and feature and particularly the sculptures on the outer sporopollenin cell-wall layers. An experimental result of 98.45% classification accuracy and F1-score more than 0.98 demonstrates the effectiveness of the proposed method. In the future, such time and cost-effective facilities can be used as promising sources for phycological studies.