The determination and matching analysis of pinch point temperature difference in evaporator and condenser of organic rankine cycle for mixed working fluid

Exergo-economic analysis of the pinch point temperature difference (PPTD) in both evaporator and condenser of sub-critical organic Rankine cycle system (ORCs) are performed based on the first and second laws of thermodynamics. Taking mixture R13I1/R601a as a working fluid and the annual total cost per net output power Z as exergo-economic performance evaluation criterion, the effects of PPTD in evaporator ΔT_e, and the PPTD ratio of condenser to evaporator y, on the exergo-economic performance of ORCs are analyzed. Moreover, how some other parameters influence the optimal PPTD in evaporator ΔT_e,opt and the optimal PPTD ratio of condenser to evaporator y_opt are also discussed. It has been found that the exergo-economic performance of ORCs is remarkably influenced by ΔT_e and y, and there exists ΔT_e,opt and y_opt. In addition, ΔT_e,opt and y_opt are affected by heat transfer coefficient ratio of condenser to evaporator ß, the temperature of working fluid at dew point in condenser T_1a, and composition of R13I1/R601a: larger ß and T_1a lead to lower ΔT_e,opt and y_opt; by contraries, larger mass fraction of R13I1 makes ΔT_e,opt and y_opt increase, and y_opt increases linearly. The effects of the temperature of working fluid at bubble point in evaporator T_3a, mass flow rate of exhaust flue gas m_g, and inlet temperature of exhaust flue gas T_gi on ΔT_e,opt and y_opt are very slight. For comparison, three additional working fluids, namely R601a, R245fa, and 0.32R245fa/0.68R601a, are also taken into account.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Clam shell catalyst for continuous production of biodiesel

Continuous flow transesterification of waste frying oil (WFO) with methanol for the biodiesel production was tested in a laboratory scale jacketed reactive distillation (RD) unit packed with clam shell based CaO as solid catalyst. The physiochemical properties of the clam shell catalysts were characterized by X-ray Diffraction (XRD), Brunauer–Emmett–Teller (BET), Scanning Electron Microscopy (SEM), and Energy Dispersive Atomic X-ray Spectrometry (EDAX). The effects of the reactant flow rate, methanol-to-oil ratio, and catalyst bed height were studied to obtain the maximum methyl ester conversion. Reboiler temperature of 65°C was maintained throughout the process for product purification and the system reached the steady state at 7 hr. The experimental results revealed that the jacketed RD system packed with clam shell based CaO showed high catalytic activity for continuous production of biodiesel and a maximum methyl ester conversion of 94.41% was obtained at a reactant flow rate of 0.2 mL/min, methanol/oil ratio of 6:1, and catalyst bed height of 180 mm.     

Modeling urban solar energy with high spatiotemporal resolution: A case study in Toronto,Canada

This research presents a method to determine the maximum potential for the capturing of solar radiation on the rooftop of buildings in an urban environment. This involves the modeling of solar energy potential and comparison to historical building energy demand profiles through the use of 3-D solar simulation software tools and geographic information systems (GIS). The objective is to accurately identify the amount of surface area that is suitable for solar photovoltaic (PV) installations and to estimate the hourly PV electricity generation potential of existing building rooftops in an urban environment. This study demonstrates a viable approach for modeling urban solar energy and offers valuable information for electricity distributors, policy makers, and urban energy planners to facilitate the substantial design of a green built environment. The developed methodology is comprised of three main sections: (1) determination of suitable rooftop area, (2) determination of the amount of incident solar radiation available per rooftop, and (3) estimation of hourly solar PV electricity generation potential. A case study was performed using this method for Ryerson University, located in Toronto, Canada. It was found that solar PV could supply up to 19% of the study area’s electricity demands during peak consumption hours. The potential benefits of solar PV was also estimated based upon hourly greenhouse gas emission intensity factors as well as Time-of-Use (TOU) savings through the Ontario Feed-in-Tariff (FIT) program, which allows for better representation of the positive impacts of solar technologies.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

Molecular tools for bathing water assessment in Europe: Balancing social science research with a rapidly developing environmental science evidence-base

The use of molecular tools, principally qPCR, versus traditional culture-based methods for quantifying microbial parameters (e.g., Fecal Indicator Organisms) in bathing waters generates considerable ongoing debate at the science–policy interface. Advances in science have allowed the development and application of molecular biological methods for rapid (~2 h) quantification of microbial pollution in bathing and recreational waters. In contrast, culture-based methods can take between 18 and 96 h for sample processing. Thus, molecular tools offer an opportunity to provide a more meaningful statement of microbial risk to water-users by providing near-real-time information enabling potentially more informed decision-making with regard to water-based activities. However, complementary studies concerning the potential costs and benefits of adopting rapid methods as a regulatory tool are in short supply. We report on findings from an international Working Group that examined the breadth of social impacts, challenges, and research opportunities associated with the application of molecular tools to bathing water regulations.     

Soil pH effects on the interactions between dissolved zinc,non-nano- and nano-ZnO with soil bacterial communities

Zinc oxide nanoparticles (ZnO NPs) are used in an array of products and processes, ranging from personal care products to antifouling paints, textiles, food additives, antibacterial agents and environmental remediation processes. Soils are an environment likely to be exposed to manmade nanoparticles due to the practice of applying sewage sludge as a fertiliser or as an organic soil improver. However, understanding on the interactions between soil properties, nanoparticles and the organisms that live within soil is lacking, especially with regards to soil bacterial communities. We studied the effects of nanoparticulate, non-nanoparticulate and ionic zinc (in the form of zinc chloride) on the composition of bacterial communities in soil with a modified pH range (from pH 4.5 to pH 7.2). We observed strong pH-dependent effects on the interaction between bacterial communities and all forms of zinc, with the largest changes in bacterial community composition occurring in soils with low and medium pH levels (pH 4.8 and 5.9). The high pH soil (pH 7.2) was less susceptible to the effects of zinc exposure. At the highest doses of zinc (2500 mg/kg dw soil), both nano and non-nano particulate zinc applications elicited a similar response in the soil bacterial community, and this differed significantly to the ionic zinc salt treatment. The results highlight the importance of considering soil pH in nanotoxicology studies, although further work is needed to determine the exact mechanisms controlling the toxicity and fate and interactions of nanoparticles with soil microbial communities.     

Production and optimization of biodiesel using mixed immobilized biocatalysts in packed bed reactor

Vegetable oils are used as raw materials for biodiesel production using transesterification reaction. Several methods for the production of biodiesel were developed using chemical (alkali and acidic compounds) and biological catalysts (lipases). Biodiesel production catalyzed by lipases is energy and cost-saving processes and is carried out at normal temperature and pressure. The need for an efficient method for screening larger number of variables has led to the adoption of statistical experimental design. In the present study, packed bed reactor was designed to study with mixed immobilized biocatalysts to have higher productivity under optimum conditions. Contrary to the single-step acyl migration mechanism, a two-step stepwise reaction mechanism involving immobilized Candida rugosa lipase and immobilized Rhizopus oryzae cells was employed for the present work. This method was chosen because enzymatic hydrolysis followed by esterification can tolerate high free fatty acid containing oils. The effects of flow rate and bed height on biodiesel yield were studied using two factors five-level central composite design (CCD) and response surface methodology (RSM). Maximum biodiesel yield of 85 and 81 % was obtained for jatropha oil and karanja oil with the optimum bed height and optimum flow rate of 32.6 cm and 1.35 L/h, and 32.6 cm and 1.36 L/h, respectively.     

Preparation of gasification feedstock from leafy biomass

Dried leaves are a potential source of energy although these are not commonly used beside to satisfy daily energy demands in rural areas. This paper aims at preparing a leafy biomass feedstock in the form of briquettes which can be directly used for combustion or to extract the combustible gas using a gasifier. Teak (Tectona grandis) and rubber (Hevea brasiliensis) leaves are considered for the present study. A binder-assisted briquetting technique with tapioca starch as binder is adopted. Properties of these leafy biomass briquettes such as moisture content, calorific value, compressive strength, and shatter index are determined. From the study, briquettes with biomass-to-binder ratio of 3:5 are found to be stable. Higher mass percentage of binder is considered for preparation of briquettes due to the fact that leafy biomasses do not adhere well on densification with lower binder content. Ultimate analysis test is conducted to analyze the gasification potential of the briquettes. Results show that the leafy biomass prepared from teak and rubber leaves has calorific values of 17.5 and 17.8 MJ/kg, respectively, which are comparable with those of existing biomass feedstock made of sawdust, rice husk, and rice straw.     

Glyphosate input modifies microbial community structure in clear and turbid freshwater systems

Since it was commercially introduced in 1974, glyphosate has been one of the most commonly used herbicides in agriculture worldwide, and there is growing concern about its adverse effects on the environment. Assuming that glyphosate may increase the organic turbidity of water bodies, we evaluated the effect of a single application of 2.4?±?0.1 mg l^?1 of glyphosate (technical grade) on freshwater bacterioplankton and phytoplankton (pico, micro, and nanophytoplankton) and on the physical and chemical properties of the water. We used outdoor experimental mesocosms under clear and oligotrophic (phytoplanktonic chlorophyll a?=?2.04 μg l^?1; turbidity?=?2.0 NTU) and organic turbid and eutrophic (phytoplanktonic chlorophyll a?=?50.3 μg l^?1; turbidity?=?16.0 NTU) scenarios. Samplings were conducted at the beginning of the experiment and at 1, 8, 19, and 33 days after glyphosate addition. For both typologies, the herbicide affected the abiotic water properties (with a marked increase in total phosphorus), but it did not affect the structure of micro and nanophytoplankton. In clear waters, glyphosate treatment induced a trend toward higher bacteria and picoeukaryotes abundances, while there was a 2 to 2.5-fold increase in picocyanobacteria number. In turbid waters, without picoeukaryotes at the beginning of the experiment, glyphosate decreased bacteria abundance but increased the number of picocyanobacteria, suggesting a direct favorable effect. Moreover, our results show that the impact of the herbicide was observed in microorganisms from both oligo and eutrophic conditions, indicating that the impact would be independent of the trophic status of the water body.