Production of diesel-range oil through pyrolysis of polyolefins recovered from municipal solid waste.

Pyrolysis is an effective method to valorize plastic waste and obtain value-added fuels. This study adopted the ANN-GA (artificial neural network-genetic algorithm) coupled with a central composition factorial design to optimize the oil production from the pyrolysis of waste polyolefins (WP). The interactive effects of PE mass fraction (20-80 wt%), residence time (20-60 min), and carrier gas flow rate (0-100 mL/min) on the yields of WP pyrolysis products were investigated extensively by ANN. Moreover, the highest WP pyrolysis oil production of 78.87 wt%, optimized by GA, was obtained under 80 wt% PE, 60 min, and 0 mL/min. It was found that the different conditions of PE mass fraction, residence time, and carrier gas flow rate did not change the types of oil's main functional groups (-CH2-, -C=C-, -C=CH2, -CH3, and =C-H). The conditions affected the WP pyrolysis oil fractions significantly. The highest diesel selectivity of 91.42% was obtained under 20 wt% PE, 20 min, and 0 mL/min. Additionally, according to the interactive effects of different conditions on the productions of WP pyrolysis products, the pyrolysis pathways were proposed to understand the pyrolysis mechanism of WP better.

Integrating in-bed gas looping and CO2 capture in the FeD-Latrine.

Sanitation equity and climate actions are world concerns stated by the United Nations in the Sustainable Development Goals. A significant source of greenhouse gas emissions is inputted by human wastes, either in developing countries through wastewater treatment plants, or in the underdeveloped world, through anaerobic digestion of fecal sludge in pit latrines. For the first time, an integrated process for CO2 reduction and capture is implemented in a thermally sustainable, latrine-like device that destroys fresh human feces using smoldering combustion, the FeD-Latrine. A gas looping oxidizes combustible gases and creates favorable conditions to capture CO2 in bed. CH4 and H2 molar fractions are decreased around 90 % and 30 %, respectively. CaO used as a sorbent captures up to 8 mmol of CO2 per gram, forming a stable CaCO3. Compared to kinetic-dominant processes for CO2 capture, we obtain an efficiency of around 52 %. Our findings show that using the FeD-Latrine to replace typical pit latrines reduces 60 % of the CO2-eq emissions.

Signal-to-noise ratio variance impact on the image reconstruction of electrical resistance tomography in solutions with high background conductivity.

Electrical Resistance Tomography (ERT) has the potentialities of non-intrusive techniques and high temporal resolution which are essential characteristics for multiphase flow measurements. However, high background conductivities, such as saline water in oil extraction, impose a limitation in ERT image reconstruction. Focusing on the operational limits of an ERT tomography system operating in different conductivity backgrounds from 0.010 to 4.584 S/m, the impact on the image reconstruction was assessed via signal-to-noise variance. The signal-to-noise ratio (SNR) variance had a strong correlation (p-value = 5.40 × 10-15) with the image reconstruction quality at the threshold of 30 dB, reaching a correlation value of r = -0.92 in the range of 0.010-0.246 S/m. Regarding the position error of the phantom, p-value = 1.30 × 10-5 and r = -0.66 were attained. The global results revealed that the correlation of the mean of the SNR (p-value = 5 × 10-4 and r = 0.55) was kept unaltered through the whole conductivity range, showing that such a statistical index can induce bias in establishing the operational limits of the hardware.

Application of a neural fuzzy model combined with simulated annealing algorithm to predict optimal conditions for polyethylene waste non-isothermal pyrolysis.

In the present study, the waste polyethylene (PE) pyrolysis under different non-isothermal conditions was investigated to estimate the optimal conversions and pyrolysis rates. The pyrolysis study was carried out using Thermogravimetry (TG) of the virgin and the waste PE under different heating rates of 5, 10, 15 and 20 °C/min. The TG experiments indicated that the virgin and the waste PE pyrolysis processes mainly underwent in the temperature range of 390-510 °C. Subsequently, the adaptive neural fuzzy model was adopted to predict the conversions and the pyrolysis rates of the virgin and the waste PE. The optimal operating conditions in different temperature ranges were optimized by the simulated annealing algorithm (SA). Moreover, the R-squared values of the virgin PE conversions (~ 1) and pyrolysis rates (> 0.999), and the waste PE conversions (~ 1) and pyrolysis rates (> 0.999) revealed the high accuracy of the adaptive neural fuzzy model predicted results.

A conceptual scenario for the use of microalgae biomass for microgeneration in wastewater treatment plants.

Microalgae are a potential source of biomass for the production of energy, which is why the amount of research on this topic has increased in recent years. This work describes the state of the art of microalgae production from wastewater treatment plants (WWTP), its potential to generate electricity and the scale in which it is possible. The methodology used was a systematic review of the gasification of microalgae from 49 articles selected. Based on the review, a conceptual scenario for microgeneration in WWTP using as feedstock microalgae for thermal gasification was developed. The most consistent assumptions for a real scale microgeneration are microalgae production in open ponds using domestic sewage as a nutritional medium; the use of the flocculation process in process of harvesting; microalgae to energy through thermal gasification process using a downdraft gasifier. Considering a WWTP with a 3000 m3/d flux capacity, 860 kg/d of dry microalgae biomass might be produced. For which, gasification has a production potential of 0.167 kWh/m3 of treated sewage, but the energy balance is compromised by the drying process. However, when the biogas produced in anaerobic treatment enter in the model, it is possible to add a surplus of electricity of 0.14 kWh/m3 of treated sewage. Finally, a cost estimate is made for the acquisition of drying and gasification-electricity generation systems. For this scenario, the results suggest that the investments may be financially returned after five years, with additional potential for further optimization.