Enhancement of Electrochemical Capacitance of Silicon Nanowires Arrays (SiNWs) by Modification with Manganese Dioxide MnO2

Silicon - Silicon nanowire arrays (SiNWs) were fabricated by one-step metal-assisted chemical etching process and modified with MnO2 nanoparticles by a chemical electroless method. Scanning...     

A fuzzy bi-objective mixed-integer programming method for solving supply chain network design problems under ambiguous and vague conditions

Supply chain (SC) network design problems are complex problems with multi-layer levels and dynamic relationships which involve a considerable amount of uncertainty concerning customer demand, facility capacity, or lead times, among others. A large number of optimization methods (i.e., fuzzy mathematical programming, stochastic programming, and interval mathematical programming) have been proposed to cope with the uncertainties in SC network design problems. We propose a fuzzy bi-objective mixed-integer linear programming (MILP) model to enhance the material flow in dual-channel, multi-item, and multi-objective SCs with multiple echelons under both ambiguous and vague conditions, concurrently. We use a computationally efficient ranking method to resolve the ambiguity of the parameters and propose two methods for resolving the vagueness of the objective functions in the proposed fuzzy MILP model. The preferences of the decision makers (DMs) on the priority of the fuzzy goals are represented with crisp importance weights in the first method and fuzzy preference relations in the second method. The fuzzy preference relations in the second method present a unique practical application of type-II fuzzy sets. The performance of the two methods is compared using comprehensive statistical analysis. The results show the perspicuous dominance of the method which uses fuzzy preference relations (i.e., type-II fuzzy sets). We present a case study in the food industry to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms. To the best of our knowledge, a concurrent interpretation of both ambiguous and vague uncertainties, which is applicable to many real-life problems, is novel and has not been reported in the literature.     

Ozone Oxidation of Pulp and Paper Wastewater and its Impact on Molecular Weight Distribution of Organic Matter

The impact of ozone oxidation on removing high molecular weight (HMW) organics in order to improve the biodegradability of alkaline bleach plant effluent was investigated using a semi-batch reactor under different initial pH (12 and 7). After the ozonation process, the ratio of BOD₅/COD increased from 0.07 to 0.16 and 0.22 for initial pH 12 and 7, respectively. Also, the effluent color decreased by 48% and 61% at initial pH 12 and pH 7, respectively. These changes were primarily driven by reductions of the HMW fractions of the effluent during ozonation.     

Fuzzy stochastic data envelopment analysis with application to base realignment and closure (BRAC)

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision-making units (DMUs) on the basis of multiple inputs and outputs. Conventional DEA models assume that inputs and outputs are measured by exact values on a ratio scale. However, the observed values of the input and output data in real-world problems are often vague or random. Indeed, decision makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Several researchers have proposed various fuzzy methods for dealing with the ambiguous and random data in DEA. In this paper, we propose three fuzzy DEA models with respect to probability-possibility, probability-necessity and probability-credibility constraints. In addition to addressing the possibility, necessity and credibility constraints in the DEA model we also consider the probability constraints. A case study for the base realignment and closure (BRAC) decision process at the U.S. Department of Defense (DoD) is presented to illustrate the features and the applicability of the proposed models.     

A new network epsilon-based DEA model for supply chain performance evaluation

Supply chain performance evaluation problems are inherently complex problems with multilayered internal linking activities and multiple entities. Data Envelopment Analysis (DEA) has been used to evaluate the relative performance of organizational units called Decision Making Units (DMUs). However, the conventional DEA models cannot take into consideration the complex nature of supply chains with internal linking activities. Network DEA models using radial measures of efficiency are used for supply chain performance evaluation problems. However, these models are not suitable for problems where radial and non-radial inputs and outputs must be considered simultaneously. DEA models using Epsilon-Based Measures (EBMs) of efficiency are proposed for a simultaneous consideration of radial and non-radial inputs and outputs. We extend the EBM model and propose a new Network EBM (NEBM) model. The proposed NEBM model combines the radial and non-radial measures of efficiency into a unified framework for solving network DEA problems. A case study is presented to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed method to a supply chain performance evaluation problem in the semiconductor industry.     

An efficient model-based branch-and-price algorithm for unrelated-parallel machine batching and scheduling problems

Journal of Scheduling - This paper presents the problem of batching and scheduling jobs belonging to incompatible job families on unrelated-parallel machines. More specifically, we investigate...     

In-situ identification of iron electrocoagulation speciation and application for natural organic matter (NOM) removal

In this work, iron speciation in electrocoagulation (EC) was studied to determine the impact of operating parameters on natural organic matter (NOM) removal from natural water. Two electrochemical EC parameters, current density (i) and charge loading rate (CLR), were investigated. Variation of these parameters led to a near unity current efficiency (φ = 0.957 ± 0.03), at any combination of i in a range of 1–25 mA/cm² and CLR in a range of 12–300 C/L/min. Higher i and CLR led to a higher bulk pH and limited the amount of dissolved oxygen (DO) reduced at the cathode surface due to mass transfer limitations. A low i (1 mA/cm²) and intermediate CLR (60 C/L/min) resulted in low bulk DO (<2.5 mg/L), where green rust (GR) was identified by in-situ Raman spectroscopy as the primary crystalline electrochemical product. Longer electrolysis times at higher i led to magnetite (Fe₃O₄) formation. Both higher (300 C/L/min) and lower (12 C/L/min) CLR values led to increased DO and/or increased pH, with lepidocrocite (γ-FeOOH) as the only crystalline species observed. The NOM removal of the three identified species was compared, with conditions leading to GR formation showing the greatest dissolved organic carbon removal, and highest removal of the low apparent molecular weight (<550 Da) chromophoric NOM fraction, determined by high performance size exclusion chromatography.