Biodiesel production and exploring properties of Datura stramonium L. oil with its optimization using combined approaches-Taguchi, grey relational analysis, and response surface methodology.

Biodiesel production through the synthesis of Datura stramonium L. oil is studied to explore the most efficient approaches to suggest an alternate feedstock for biodiesel production. The main objective of this work is to optimize the process variables of biodiesel synthesis by using some statistical approach (Taguchi method, grey relational analysis (GRA), and response surface methodology (RSM) analyzing three parameters, i.e., alcohol-to-oil molar ratio, catalyst (NaOH) concentration, and process temperature for achieving maximum biodiesel derived from Datura stramonium L. oil. The transesterification process is applied by using an ultrasonic-assisted technique. Grey relational analysis (GRA) was successfully applied with the Taguchi method resulting in the optimum combination of A2B1C1. Based on the findings, the best operating conditions for transesterifying are attained with the RSM approach consisting of a 5.697:1 molar ratio (level 2), 0.3 (wt.%) NaOH concentration (level 1), and 70 °C process temperature (level 1). With a value of 87.02%, these ideal operating conditions produce the maximum yield as compared to grey relational analysis (GRA) yields 83.99%. The obtained results have been verified through the characterization of oil and biodiesel as well. Also, the fuel qualities of DSL biodiesel were identified and assessed. DSL oil was found 137.6 degrees of unsaturation during fatty acid profile analysis. DSL biodiesel was found the best kinematic viscosity (4.2 mm2/s) and acid value (0.49) when compared to Karanja and palm biodiesel. D. stramonium L. was recognized as a suitable species for biodiesel feedstock according to the findings.

Dual pretreatment of mixing H2O2 followed by torrefaction to upgrade spent coffee grounds for fuel production and upgrade level identification of H2O2 pretreatment.

Biochar is a carbon-neutral solid fuel and has emerged as a potential candidate to replace coal. Meanwhile, spent coffee grounds (SCGs) are an abundant and promising biomass waste that could be used for biochar production. This study develops a biochar valorization strategy by mixing SCGs with hydrogen peroxide (H2O2) at a weight ratio of 1:0.75 to upgrade SCG biochar. In this dual pretreatment method, the H2O2 oxidative ability at a pretreatment temperature of 105 °C contributes to an increase in the higher heating value (HHV) and carbon content of the SCG biochars. The HHV and carbon content of biochar increase by about 6.5% and 7.8%, respectively, when compared to the unpretreated one under the same conditions. Maximized biochar's HHV derived via the Taguchi method is 30.33 MJkg-1, a 46.9% increase compared to the raw SCG, and a 6.5% increase compared to the unpretreated SCG biochar. The H2O2 concentration is 18% for the maximized HHV. A quantitative identification index of intensity of difference (IOD) is adopted to evaluate the contributive level of H2O2 pretreatment in terms of the HHV and carbon content. IOD increases with increasing H2O2 pretreatment temperature. Before torrefaction, SCGs' IOD pretreated at 50 °C is 1.94%, while that pretreated at 105 °C is 8.06%. This is because, before torrefaction, H2O2 pretreatment sufficiently weakens SCGs' molecular structure, resulting in a higher IOD value. The IOD value of torrefied SCGs (TSCG) pretreated at 105 °C is 10.71%, accounting for a 4.59% increase compared to that pretreated at 50 °C. This implies that TSCG pretreated by H2O2 at 105 °C has better thermal stability. For every 1% increase in IOD of TSCG, the carbon content of the biochar increases 0.726%, and the HHV increases 0.529%. Overall, it is demonstrated that H2O2 is a green and promising pretreatment additive for upgrading SCG biochar's calorific value, and torrefied SCGs can be used as a potential solid fuel to approach carbon neutrality.

Integrating Taguchi method and artificial neural network for predicting and maximizing biofuel production via torrefaction and pyrolysis.

Artificial neural network (ANN) is one kind of artificial intelligence in the computing system that aims to process information as the way neurons in the human brain. In this study, the combination of the Taguchi method and ANN are used to maximize and predict biofuel yield from spent mushroom substrate torrefaction and pyrolysis via microwave irradiation. The Taguchi method is utilized to design the multiple factors (particle size, catalyst, power, and magnetic agent) and levels of experiment parameters. The highest total biofuel yield (biochar + bio-oil) is 99.42%, accomplished by a combination of 355 µm particle size, 300 mg·g-SMS-1 catalyst, 900 W power, and 300 mg·g-SMS-1 magnetic agent. ANN with one hidden layer shows the outstanding linear regression predictions for the highest biofuel yields (biochar 0.9999 and bio-oil 0.9998). This high linear regression indicates that ANN with a quick propagation algorithm is an appropriate approach for predicting biofuel conversion.

Effect of Different Lubricating Environment on the Tribological Performance of CNT Filled Glass Reinforced Polymer Composite.

In recent years, the engineering implications of carbon nanotubes (CNTs) have progressed enormously due to their versatile characteristics. In particular, the role of CNTs in improving the tribological performances of various engineering materials is well documented in the literature. In this work, an investigation has been conducted to study the tribological behaviour of CNTs filled with glass-reinforced polymer (GFRP) composites in dry sliding, oil-lubricated, and gaseous (argon) environments in comparison to unfilled GFRP composites. The tribological study has been conducted on hardened steel surfaces at different loading conditions. Further, the worn surfaces have been examined for a particular rate of wear. Field-emission scanning electron (FESEM) microscopy was used to observe wear behaviours. The results of this study explicitly demonstrate that adding CNTs to GFRP composites increases wear resistance while lowering friction coefficient in all sliding environments. This has also been due to the beneficial strengthening and self-lubrication properties caused by CNTs on GFRP composites, according to FESEM research.

An evaluation of thermal characteristics of bacterium Actinobacillus succinogenes for energy use and circular bioeconomy.

The thermal characteristics of Actinobacillus succinogenes (AS) from pyrolysis, torrefaction, and combustion are analyzed to evaluate the potential of this biomass as a renewable fuel. AS pyrolysis can be classified into four stages, and its main decomposition zone is at 200-500 °C. The solid yield of AS after 60 min torrefaction is over 60 wt%, and the torrefaction severity index map indicates that a high torrefaction temperature with a short duration has a more profound influence on its decomposition. The Py-GC/MS analysis of AS suggests that the volatile products from 500 °C pyrolysis are similar to microalgae-derived pyrolysis bio-oils. The combustibility index (S) of AS is 4.07 × 10-7 which is much higher than that of lignite coal (0.39 × 10-7) and bituminous coal (0.18 × 10-7), and close to those of biochar and bio-oil. The obtained results are conducive to the development of microorganisms as fuel to achieve a circular bioeconomy.

Non-edible vegetable oil-based feedstocks capable of bio-lubricant production for automotive sector applications-a review.

Fossil fuel resource is on the draining stage which leads to an increment in the cost of the petroleum products. Nowadays, research is focused on the development of environment-friendly lubricants which are derivatives of renewable sources. Bio-lubricants based on non-edible oil sources are environmentally friendly because they are non-hazardous and biodegradable and no emission of toxic gases were detected when they are used. This study involves the characterizations and advantages, as well as utilization of inedible plant oil-driven bio-lubricants as an alternative for tribological applications. This report also presents the status of the global lubricant market as well as the potential outlook of the bio-lubricants for their future usage. Non-edible plant oil-driven bio-lubricants bear high viscosity, high lubricity, and high viscosity index which can enhance the equipment service life and deserve the ability to carry the high load and results in a minimum amount of metal traces during combustion while applied to engines. Beside their advantages, some of the disadvantages are also there which can be addressed by the employment of certain additives available according to the applications. The detailed study about the different additives utilized during their use in the internal combustion engine is also described in detail during this study. This study provides a detailed description of the possibilities associated with bio-lubricant based on non-edible oil feedstocks to the automotive sector applications.

Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm.

In this paper, an adaptive artefact canceller is designed using the bounded range artificial bee colony (BR-ABC) optimization technique. The results of proposed method are compared with recursive least square and other evolutionary algorithms. The performance of these algorithms is evaluated in terms of signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME) mean, standard deviation (SD) and correlation factor (r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. A comparative study of various techniques reveals that the performance of BR-ABC algorithm is better in noisy environment. Our simulation results show that the ANC filter using BR-ABC technique provides 15 dB improvement in output average SNR, 63 and 83% reduction in MSE and ME, respectively as compared to ANC filter based on PSO technique. Further, the ANC filter designed using BR-ABC technique enhances the correlation between output and pure EMG signal.