Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

Investigating effect of proton-exchange membrane on new air-cathode single-chamber microbial fuel cell configuration for bioenergy recovery from Azorubine dye degradation

Environmental Science and Pollution Research - One of the biggest challenges of using single-chamber microbial fuel cells (MFCs) that utilize proton-exchange membrane (PEM) air cathode for...     

Modification of photocatalytic property of BaTiO3 perovskite structure by Fe2O3 nanoparticles for CO2 reduction in gas phase

In this work, perovskite structure of BaTiO₃ was coupled with Fe₂O₃ in different molar ratios achieving the best photocatalytic performance of CO₂ reduction in the presence of CH₄ as reducing agent; both of them are main greenhouse gases. The photocatalysts were synthesized by facile hydrothermal method. The samples were characterized by XRD, FTIR, FESEM, EDX, UV–Vis DRS, and photoluminescence (PL) analyses. The BaTiO₃ synthesized in this research showed a weak PL signal which is due to the intrinsic ferroelectric property as has been observed in previous reports. Compared to the pure BaTiO₃ and Fe₂O₃, the heterojunctions exhibited enhanced photocatalytic activity. The maximum CO₂ reduction under visible light irradiation was obtained to be 22% during 60 min process time. The enhanced photocatalytic activity could be attributed to the increased optical absorption, the good separation, and immigration of photogenerated charge carriers that decreased the recombination rate of charge carriers in the n–n heterojunction.

     

Correlation between lead exposure and cognitive function in 12-year-old children: a systematic review and meta-analysis

Environmental Science and Pollution Research - Lead exposure as a toxic material especially in children can be recognized as a harmful factor for cognitive function system. This meta-analysis was...     

Fabrication of Nano Zero valent Iron/Biopolymer Composite with Antibacterial Properties for Simultaneous Removal of Nitrate and Humic Acid: Kinetics and Isotherm Studies

Journal of Polymers and the Environment - This investigation compared the adsorption behavior of humic acid (HA) on cellulose, chitosan and nano zerovalent iron/chitosan (nZVI/chitosan). Results...