Optimization of clavulanic acid production by Streptomyces daufpe 3060 by response surface methodology

Clavulanic acid is a β-lactam antibiotic which has a potent β-lactamase inhibiting activity. In order to optimize its production by the new isolate Streptomyces DAUFPE 3060, the influence of two independent variables, temperature and soybean flour concentration, on clavulanic acid and biomass concentrations was investigated in 250 mL-Erlenmeyers according to a 2² central composite design. To this purpose, temperature and soybean flour (SF) concentration were varied in the ranges 26-34°C and 10-50 g/L, respectively, and the results evaluated utilizing the Response Surface Methodology. The experimental maximum production of clavulanic acid (629 mg/L) was obtained at 32°C and 40 g/L SF after 48 h, while the maximum biomass concentration (3.9 g/L) at 30°C and 50 g/L soybean flour, respectively. These values are satisfactorily close to those (640 mg/L and 3.75 g/L, respectively) predicted by the model, thereby demonstrating the validity of the mathematical approach adopted in this study.Brazilian Research Funding InstitutionsCoordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES

Tools for Optimization of Biomass-to-Energy Conversion Processes

Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conversion as general components, can be costly and uncertain due to inherent variability. Optimization methods are widely applied for modeling the biomass supply chain (BSC) for energy processes. In this qualitative review, the main aspects and global trends of using geographic information systems (GISs), linear programming (LP) and neural networks to optimize the BSC are presented. Modeling objectives and factors considered in studies published in the last 25 years are reviewed, enabling a broad overview of the BSC to support decisions at strategic, tactical and operational levels. Combined techniques have been used for different purposes: GISs for spatial analyses of biomass; neural networks for higher heating value (HHV) correlations; and linear programming and its variations for achieving objectives in general, such as costs and emissions reduction. This study reinforces the progress evidenced in the literature and envisions the increasing inclusion of socio-environmental criteria as a challenge in future modeling efforts

Bioengineering of the plant culture of Capsicum frutescens with vanillin synthase gene for the production of vanillin

Production of vanillin by bioengineering has gained popularity due to consumer demand towards vanillin produced by biological systems. Natural vanillin from vanilla beans is very expensive to produce compared to its synthetic counterpart. Current bioengineering works mainly involve microbial biotechnology. Therefore, alternative means to the current approaches are constantly being explored. This work describes the use of vanillin synthase (VpVAN), to bioconvert ferulic acid to vanillin in a plant system. The VpVAN enzyme had been shown to directly convert ferulic acid and its glucoside into vanillin and its glucoside, respectively. As the ferulic acid precursor and vanillin were found to be the intermediates in the phenylpropanoid biosynthetic pathway of Capsicum species, this work serves as a proof-of-concept for vanillin production using Capsicum frutescens (C. frutescens or hot chili pepper). The cells of C. frutescens were genetically transformed with a codon optimized VpVAN gene via biolistics. Transformed explants were selected and regenerated into callus. Successful integration of the gene cassette into the plant genome was confirmed by polymerase chain reaction. High performance liquid chromatography was used to quantify the phenolic compounds detected in the callus tissues. The vanillin content of transformed calli was 0.057% compared to 0.0003% in untransformed calli

Doxycycline degradation by the oxidative Fenton process

Doxycycline is a broad-spectrum tetracycline occurring in domestic, industrial and rural effluents, whose main drawback is the increasing emergence of resistant bacteria. This antibiotic could be degraded by the so-called Fenton process, consisting in the oxidation of organic pollutants by oxygen peroxide (H2O2) in the presence of Fe2+. Experiments were performed according to an experimental Rotational Central Composite Design to investigate the influence of temperature (0 \u2013 40.0\ub0C), H2O2 concentration (100 \u2013 900 mg/L) and Fe2+ concentration (5 \u2013 120 mg/L) on residual doxycycline and total organic carbon concentrations. Whereas the final residual doxycycline concentration ranged from 0 to 55.8 mg/L, the oxidation process proved unable to reduce the total organic carbon by more than 30%. The best operating conditions were concentrations of H2O2 and Fe2+ of 611 and 25 mg/L, respectively, and temperature of 35.0\ub0C, but the analysis of variance revealed that only the first variable exerted a statistically-significant effect on the residual doxycycline concentration. These results suggest possible application of this process in the treatment of doxycycline-containing effluents and may be used as starting basis to treat tetracycline-contaminated effluents

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations

A Novel Remaining Useful Estimation Model to Assist Asset Renewal Decisions Applied to the Brazilian Electric Sector

Assets deteriorate over time, as well as being covered, corroded, or becoming old in less obvious ways. Maintenance can extend the remaining useful life (RUL) of an asset system, but sooner or later it must surely be replaced. In this study, we propose a new RUL estimation methodology to assist in decision making for the maintenance and replacement of assets from prioritizing equipment in a renovation plan. Our methodology uses advanced data analysis techniques that consider multiple competing criteria with the goal of maximizing values of the asset throughout its life cycle, while considering the rules of remuneration and service quality of the current regulation, as well as the values at risk according to the decisions and actions taken. Experimental results with real datasets show the efficiency of the proposed approach. Finally, this work also presents the development of an analytical tool to optimize asset renewal decisions applying the RUL estimation methodology proposed and its application to the Brazilian electric sector

Self-Healing Concrete: Concepts, Energy Saving and Sustainability

The production of cement accounts for 5 to 7% of carbon dioxide emissions in the world, and its broad-scale use contributes to climate imbalance. As a solution, biotechnology enables the cultivation of bacteria and fungi for the synthesis of calcium carbonate as one of the main constituents of cement. Through biomineralization, which is the initial driving force for the synthesis of compounds compatible with concrete, and crystallization, these compounds can be delivered to cracks in concrete. Microencapsulation is a method that serves as a clock to determine when crystallization is needed, which is assisted by control factors such as pH and aeration. The present review addresses possibilities of working with bioconcrete, describing the composition of Portland cement, analysis methods, deterioration, as well as environmental and energetic benefits of using such an alternative material. A discussion on carbon credits is also offered. The contents of this paper could strengthen the prospects for the use of self-healing concrete as a way to meet the high demand for concrete, contributing to the building of a sustainable society