An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

This paper introduces an integrated approach based on data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT) for total energy efficiency assessment and optimization in energy intensive manufacturing sectors. Total energy efficiency assessment and optimization of the proposed approach considers structural indicators in addition conventional consumption and manufacturing sector output indicators. The validity of the DEA model is verified and validated by PCA and NT through Spearman correlation experiment. Moreover, the proposed approach uses the measure-specific super-efficiency DEA model for sensitivity analysis to determine the critical energy carriers. Four energy intensive manufacturing sectors are discussed in this paper: iron and steel, pulp and paper, petroleum refining and cement manufacturing sectors. To show superiority and applicability, the proposed approach has been applied to refinery sub-sectors of some OECD (Organization for Economic Cooperation and Development) countries. This study has several unique features which are: (1) a total approach which considers structural indicators in addition to conventional energy efficiency indicators; (2) a verification and validation mechanism for DEA by PCA and NT and (3) utilization of DEA for total energy efficiency assessment and consumption optimization of energy intensive manufacturing sectors.     

Preparation of polymer-coated, scintillating ion-exchange resins for monitoring of 99Tc in groundwater

The present study was oriented to prepare new scintillating anion-exchange resins for measurement of (99)TcO(4)(-) in natural waters. The organic fluor 2-(1-naphthyl)-5-phenyloxazole was diffused into (chloromethyl)polystyrene resin. Thereafter, a thin layer of poly[[2-(methacryloyloxy)ethyl]trimethylammonium chloride] was grafted from the resin surface by surface-initiated atom transfer radical polymerization as an attempt to overcome potential problems related to the leaching of fluor molecules during usage. The residual chloromethyl groups of the polymer-coated resin were aminated by reaction with two different tertiary amines, triethylamine (TEA) and methyldioctylamine (MDOA). Off- and on-line quantification of (99)Tc was achieved with high detection efficiencies of 60.72 ± 1.93% and 72.83 ± 0.81% for resin with TEA and MDOA functional groups, respectively. The detection limit was determined to be less than the maximum contaminant level (33 Bq L(-1)) established under the Safe Drinking Water Act. The two functionalized resins were demonstrated to be selective for pertechnetate from synthetic groundwater containing up to 1000 ppm Cl(-), SO(4)(2-), and HCO(3)(-) and up to 1200 ppb Cr(2)O(7)(2-) in an acidic medium.     

Study of Physically Transient Insulating Materials as a Potential Platform for Transient Electronics and Bioelectronics

Controlled degradation and transiency of materials is of significant importance in the design and fabrication of degradable and transient biomedical and electronic devices and platforms. Here, the synthesis of programmable biodegradable and transient insulating polymer films is reported, which have sufficient physical and chemical properties to be used as substrates for the construction of transient electronics. The composite structure can be used as a means to control the dissolution and transiency rate of the polymer composite film. Experimental and computational studies demonstrate that the addition of gelatin or sucrose to a PVA polymer matrix can be used as a means to program and either slow or enhance the transiency of the composite. The dissolution of the polymer composites are fitted with inverse exponential functions of different time constants; the lower time constants are an indication of faster transiency of the polymer composite. The addition of gelatin results in larger time constants, whereas the addition of sucrose generally results in smaller time constants.     

Designing a cellular manufacturing system considering decision style,skill and job security by NSGA-II and response surface methodology

Cell formation is a traditional problem in cellular manufacturing systems that concerns the allocation of parts, operators and machines to the cells. This paper presents a new mathematical programming model for cell formation in which operators’ personality and decision-making styles, skill in working with machines, and also job security are incorporated simultaneously. The model involves the following five objectives: (1) minimising costs of adding new machines to and removing machines from the cells at the beginning of each period, (2) minimising total cost of material handling, (3) maximising job security, (4) minimising inconsistency of operators’ decision styles in cells and (5) minimising cost of suitable skill. On account of the NP-hard nature of the proposed model, NSGA-II as a powerful meta-heuristic approach is used for solving large-sized problems. Furthermore, response surface methodology (RSM) is used for tuning the parameters. Lastly, MOPSO and two scalarization methods are employed for validation of the results obtained. To the best of our knowledge, this is the first study that presents a multi-objective mathematical model for cell formation problem considering operators’ personality and skill, addition and removal of machines and job security.     

An integrated fuzzy regression-data envelopment analysis algorithm for optimum oil consumption estimation with ambiguous data

This study introduces an integrated fuzzy regression (FR) data envelopment analysis (DEA) algorithm for oil consumption estimation and optimization with uncertain and ambiguous data. This is quite important as oil consumption estimations deals with several uncertainties due to social, economic factors. Furthermore, DEA is integrated with FR because there is no clear cut as to which FR approach is superior for oil consumption estimation. The standard indicators used in this paper are population, cost of crude oil, gross domestic production (GDP) and annual oil production. Fifteen popular and most cited FR models are considered in the algorithm. Each FR model has different approach and advantages. The input data is divided into train and test data. The FR models have been tuned for all their parameters according to the train data, and the best coefficients are identified. Center of Average Method for defuzzification output process is applied. For determining the rate of error of FR models estimations, the rate of defuzzified output of each model is compared with its actual rate consumption in test data. The efficiency of 15 FR models is examined by the output-oriented Data Envelopment Analysis (DEA) model without inputs by considering three types of relative error: RMSE, MAE and MAPE. The applicability and superiority of the proposed algorithm is shown for monthly oil consumption of Canada, United States, Japan and Australia from 1990 to 2005.     

Glass microparticle- versus microsphere-filled experimental dental adhesives

This study aimed to formulate antibacterial dental adhesives. Phosphate-substituted methacrylate adhesives were modified with 0–20 wt % copper-doped glass microparticles. Two shapes of microparticles were used: regular shaped (microspheres) and irregular shaped (microparticles). The morphology/composition, roughness, monomer conversion (DC%), thermogravimetric analysis, and antibacterial action against S. mutans and P. aeruginosa and ion release were investigated. The results showed that microspheres produced adhesives with a relatively smoother surface than microparticles did. The DC% of adhesives increased with increasing glass filler content. Filled adhesives showed polymer decomposition at ~315 °C and glass melting at 600–1000 °C. The weight loss percent of adhesives decreased with increasing weight percent of fillers. Glass microparticles at 0–20 wt % significantly increased the antibacterial action of adhesives against both bacteria. Glass microspheres at 0–5 wt % significantly increased the antibacterial action of adhesives against both bacteria. Only 20 wt % microparticle-filled adhesive showed an inhibition zone similar to tobramycin (positive control). Microparticle-filled adhesives (with >5 wt % filler) significantly reduced S. mutans more than their microsphere counterparts. Microsphere-filled adhesives (with ≤5 wt % filler) significantly reduced P. aeruginosa more than their microparticle counterparts. Microsphere-filled adhesives showed higher Cu release than their microparticle counterparts. Accordingly, phosphate-substituted methacrylate filled with glass could be used as an antibacterial adhesive. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47832.     

Application of saturation transfer double difference NMR to elucidate the mechanistic interactions of pesticides with humic acid

Elucidation of mechanistic interactions of anthropogenic chemicals is critical to understanding and eventually predicting their behavior in the environment Here, a recently developed technique, saturation transfer double difference (STDD) NMR spectroscopy is employed to determine the interactions of pesticides with humic acid (HA) at the molecular level. The degree of interaction at each NMR observable nucleus in the pesticide can be quantified in the form of an epitope map, which depicts the mechanism of the pesticide-HA interaction. Our results indicate that, at pH 7, halogen atoms (F and Cl) in water-soluble pesticides (diflufenzopyr, acifluorfen, and chlorsulfuron) play a dominant role in influencing binding to HA, whereas carboxyl groups likely play a secondary role when halogen atoms are also present in the molecule, as observed with diflufenzopyr and acifluorfen. However, when present on its own, the carboxyl group dominates in binding affinityto HA (e.g., imazapyr). Electronegativity and electron density appear to play a key role in the mechanism of binding and results suggest that polar bonds are the primary points of HA contact in the water soluble pesticides investigated. Likely interactions may include hydrogen bonding and dipole-dipole interactions.     

Molecular interactions of pesticides at the soil-water interface

High-resolution magic angle spinning (HR-MAS) NMR spectroscopy combined with saturation-transfer double difference (STDD) NMR can be used to analyze the molecular-level interactions of pesticides and whole soils occurring at the soil-water interface. Here 1H HR-MAS STDD NMR has been applied to some common pesticides (trifluralin, acifluorfen, and (4-nitro-3-(trifluoromethyl) phenol) and a pesticide degradation product (1-naphthol). Results indicate that dipolar interactions, H-bonding, hydrophobic associations, and potentially pi-pi interactions are the predominant sorption mechanisms for these molecules at the soil-aqueous interface. It is evident that the physical and chemical characteristics of soil are highly influential in determining the mechanisms of pesticide sorption, as they significantly affect soil conformation. In particular, different binding mechanisms were observed for 1-naphthol in soil swollen using a buffer versus D2O, indicating that the K(oc) alone may not be enough to accurately predict the behavior of a molecule in a real soil environment. Preliminary kinetic-based studies suggest that both the swelling solvent and soil moisture content significantly influence the sequestration of trifluralin. These studies demonstrate that HR-MAS and STDD NMR are powerful and versatile tools which can be applied to expand our knowledge of the mechanistic interactions of agrochemicals at the molecular level.     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.     

A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

Mobile Networks and Applications - This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is...