Due to diminishing petroleum reserves and deleterious environmental consequences of exhaust gases from fossil-based fuels, research on renewable and environment friendly fuels has received a lot of ...impetus in the recent years. However, the availability of the non-edible crops serve as the sources for biofuel production are limited and economically not feasible. Algae are a promising alternative source to the conventional feedstocks for the third generation biofuel production. There has been a considerable discussion in the recent years about the potential of microalgae for the production of biofuels, but there may be other more readily exploitable commercial opportunities for macroalgae and microalgae. This review, briefly describes the biofuels conversion technologies for both macroalgae and microalgae. The gasification process produces combustible gases such as H2, CH4, CO2 and ammonia, whereas, the product of pyrolysis is bio-oil. The fermentation product of algae is ethanol, that can be used as a direct fuel or as a gasohol. Hydrogen can be obtained from the photobiological process of algal biomass. In transesterification process, algae oil is converted into biodiesel, which is quite similar to those of conventional diesel and it can be blended with the petroleum diesel. This study, also reviewed the production of high value byproducts from macroalgae and microalgae and their commercial applications. Algae as a potential renewable resource is not only used for biofuels but also for human health, animal and aquatic nutrition, environmental applications such as CO2 mitigation, wastewater treatment, biofertilizer, high-value compounds, synthesis of pigments and stable isotope biochemicals. This review is mainly an attempt, to investigate the biorefinery concept applied on the algal technology, for the synthesis of novel bioproducts to improve the algal biofuels as even more diversified and economically competitive.
The employment of a high-value, co-product strategy through the integrated biorefinery approach is expected to significantly enhance the overall commercial implementation of the biofuel from the algal technology.
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability ...distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. We propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.
•A computationally efficient method to predict failure probability.•Our “multifidelity” approach results in 50% computational cost saving.•Our multifidelity approach is cost-aware and adaptive.•We present theoretical guarantees: unbiasedness and asymptotic convergence.•Successful demonstration on synthetic and real-world test cases.
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake ...flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional
latent
space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.
Biodiesel is mainly produced by transesterification reaction between lipid feedstock such as vegetable oil or algal oil and alcohol. Considering the depletion of conventional fossil fuel, biodiesel ...is gaining more attention as a renewable, sustainable and environmental friendly fuel. Heterogeneous catalysts are mostly applied in transesterification reaction due to many advantages such as easy catalyst separation and reusability, improved selectivity, reducing process stages and cost effective. Biodiesel process faces various problems related to immiscible nature of oil and alcohol leads to poor mass transfer rate. This requires long reaction time, higher catalyst consumption, higher methanol-oil molar ratio, high temperature and high stirring rate. This review discusses that the latest advances in ultrasonic assist transesterification reaction with the use of heterogeneous catalysts to produce biodiesel with cost effective. Ultrasonic energy can emulsify the reactants to reduce the catalyst requirement, methanol-oil ratio, reaction time and reaction temperature.
The potential of using dairy waste scum as a feed stock for bio-diesel production was investigated. Present study optimized the parameters involved in transesterification process of Dairy Waste Scum ...Oil. Gas chromatography was used to determine the fatty acid composition of Dairy Waste Scum Oil. Results revealed that the low free fatty acid content was a notorious parameter to determine the viability of alkaline transesterification. The yield of bio-diesel reached 96.7% when 1.2 wt.% of Potassium Hydroxide, reaction temperature of 75
°C, 30
min of time and 6:1 Methanol oil ratio at 350
rpm. Thermo gravimetric analysis followed the evaluation of transesterification process. The present analysis confirms that bio-diesel from dairy waste scum is quit suitable as an alternative to petroleum diesel with recommended fuel properties as per ASTM standards. This new way for using dairy waste scum reduces the cost of production of bio-diesel and the problem related to the disposal of Dairy scum.
Introduction: The Erector Spinae Plane Block (ESPB) is a novel procedure that has shown benefits in postoperative pain management for various surgeries. It involves the systemic infiltration of ...anaesthesia into the surrounding tissues, which helps to suppress local pain responses. Aim: To compare the efficacy of Ultrasound-guided (USG) ESPB with local anaesthetic infiltration in postoperative pain management for patients undergoing Percutaneous Nephrolithotomy (PCNL). The comparison was based on the Numeric Rating Scale (NRS) score and the time taken for the first rescue analgesic requirement, along with its total consumption within 24 hours. Materials and Methods: A randomised clinical study was conducted in the Department of Anaesthesia at Velammal Medical College and Hospital, Madurai, Tamil Nadu, India. The duration of the study was two months, from September 2022 to October 2022. A total of 70 patients were randomly assigned to either group L (n=35) (local anaesthetic infiltration) or group E (n=35) (USG-guided ESPB). Demographic details, NRS pain scores, time taken for the first rescue analgaesia, and total consumption within 24 hours were noted and analysed. Descriptive analysis was performed, and a comparison between the groups was made using the Mann-Whitney U test or Chisquare test. Analysis was conducted using coGuide V1.0.3. Results: The mean age (mean±SD) of the study participants in group L and group E was found to be 49.31±13.96 years and 46.37±13.72 years, respectively. A total of 35 patients were included in each group, consisting of 16 (45.71%) females and 19 (54.29%) males in both groups. The difference in NRS scores was significant at 30 minutes, one hour (p-value <0.001), and six hours (p-value <0.011). The median time required for the first rescue analgesic was found to be 480 and 30 minutes in group E and group L, respectively (p-value <0.001). The median total consumption within 24 hours was 50 mg in both groups. Conclusion: The USG-guided ESPB provided a longer-lasting analgesic effect in postoperative pain management for PCNL patients, as evidenced by the NRS pain scale, postoperative opioid consumption, and time for the first rescue analgesia
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