Ethanol Production from Soybean Fiber,a Co-product of Aqueous Oil Extraction,Using a Soaking in Aqueous Ammonia Pretreatment

The effectiveness of soaking in aqueous ammonia (SAA) as a pretreatment method for the conversion of soybean fiber to ethanol via simultaneous saccharification and fermentation (SSF) was investigated. Insoluble fiber is a co-product from oil and protein extraction using two-stage, countercurrent, enzyme-assisted, aqueous extraction processing of full-fat soybean flakes (FFSF) and extruded FFSF. The fiber fractions were soaked in 15 wt% aqueous ammonia at 1:10 solid-to-liquid ratio. The effects of operating variables, including treatment times (6, 12, and 24 h), treatment temperatures (60 and 80 °C), and cellulase loadings (15 and 60 FPU/g-glucan) on the degree of enzymatic hydrolysis were determined. The best SAA conditions were 80 °C for 12 h followed by an enzyme loading of 15 FPU/g-glucan, which produced a 152-mg/g glucose yield after 48 h of hydrolysis. This was 8.7 times the amount produced from the same fiber not pretreated with SAA. The glucose yield increased to 381 mg/g when fiber obtained from extruded FFSF was submitted to SAA. SAA (80 °C, 12 h) on extruded fiber subjected to SSF increased ethanol yield from 0.06 g of ethanol/g [40% of theoretical yield] (for non SAA pretreated fiber) to 0.25 g of ethanol/g [92% of theoretical yield]. The combination of extrusion and SAA was an efficient means for converting the fiber-rich soybean fraction into ethanol.     

Optimal analog active filter design using craziness‐based particle swarm optimization algorithm

Because of the manufacturing constraints, the optimal selection of passive component values for the design of analog active filter is very critical. As the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. Evolutionary computation may be an attractive alternative for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient evolutionary optimization approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique employed is craziness‐based particle swarm optimization (CRPSO). PSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: fast convergence and only a few control parameters. However, the performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems, the PSO algorithm has been modified to CRPSO and is used for the selection of optimal passive component values of fourth‐order Butterworth low‐pass analog active filter and second‐order state variable low‐pass filter, respectively. CRPSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low‐pass active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that CRPSO efficiently minimizes the total design error with respect to previously used optimization techniques. Copyright © 2014 John Wiley & Sons, Ltd.     

Uptake and loss of water in a cenosphere-concrete composite material

Cenospheres are hollow, aluminum silicate spheres, between 10 and 300 μm in diameter. Their low specific gravity (0.67) makes them ideal replacements for fine sand for producing low-density concrete. In an effort to understand the potential for practical use of the cenospheres as a fine aggregate in concrete, the moisture uptake and loss by cenospheres and water uptake and loss in cenosphere-concrete composites have been studied in this paper. The equilibrium moisture content of cenospheres is about 18 times higher than that of sand, reflecting the porous nature of cenospheres. The temporal evolution of water penetration into the cenosphere-concrete is modeled using Washburn kinetics. The effective pore size using this model is of the order of several nanometers. These results imply a lack of connectivity within the pores, leading to a low permeability. SEM images of the concrete reveal pore sizes of the order of 2-5 μm. The drying flux for cenospheres shows a classical behavior—a constant rate followed by a linear falling rate period. Thus, experiments done at these conditions can be used to predict drying times for wet cenospheres exposed to other environments. The flux of water vapor away from both the cenosphere-concrete as well as the normal concrete shows a nonlinear change with moisture content throughout the drying cycle, implying that the pore structure within the concrete strongly influences the drying behavior.     

Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios

Water Resources Management - Evapotranspiration is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various...     

Application of Choquet integral in interval type-2 Pythagorean fuzzy sustainable supply chain management under risk

The purpose of this study is to offer requisite models for unravelling the complex issues that arise in a supplier selection-order allocation problem by soothing the risk and disturbances. To do this, a new uncertain environment, interval type-2 Pythagorean fuzzy set (IT2PFS) is introduced to assist the experts for assuring secure and reliable outcomes in hesitant situations. The operational laws on IT2PFS under Dombi $t$-norm and $t$-conorm are defined, which are further utilized to develop geometric Bonferroni mean and Bonferroni mean operators based on Choquet integral (CI) under IT2PFS. The aggregation operators can ennoble the pliability of the information blending process through the adjustment of several parameters and can make good interactions among the criteria with their weights. Thereafter, to determine the weights of the criteria, an empirical method, namely the decision making trial and evaluation laboratory (DEMATEL) is amplified by conjoining CI and IT2PFS into it. A multicriteria decision making method, namely, the multiattribute border approximation area comparison (MABAC) and subsequently the CI based grey relational analysis method are then employed to classify the suppliers and derive their corresponding weights with reference to the sustainable criteria. Over and above, a new multiobjective optimization model is exhibited to uphold the purchasing managers for assembling suppliers keeping in mind the sustainability aspects and overall risk of suppliers by following Markowitz portfolio theory. A real-life supply chain management problem is demonstrated to elucidate the aptness and efficiency of the proposed work. Furthermore, the effectiveness and importance of the study are validated via the comparative analysis with existing approaches. The main contribution of the study is in handling the difficulty and confusion that arise during information gathering, information aggregation, suppliers evaluation and order allocation phases.     

Optimal deflection and stacking sequence prediction of curved composite structure using hybrid (FEM and soft computing) technique

Engineering with Computers - The bending deflections and the corresponding optimal fiber angle sequences of the subsequent layers have been predicted in this article using a hybrid technique. The...     

Electrophoretic deposition studies of Ba(Zr-Ce-Y)O3 ceramic coating

Electrophoretic deposition (EPD) is now a well established colloidal processing technique which uses electrophoresis mechanism for the movement of suspended charged particles in a suspension in the presence of an electric field. In this work, electrophoretic deposition of BaZr_0.4Ce_0.4Y_0.2O_3-δ (BZCY) in ethanol medium was performed under different conditions on both conducting and non-conducting (porous anode) substrate without using any external additives in a suspension bath. Process parameters such as deposition time, voltage, and rate of deposition of suspended particles were studied under various conditions. Green coating deposited under different potential (30, 50, and 70V) was uniform and crack free, even at extended time of deposition. Surface roughnesses have also been evaluated to correlate it with deposition conditions. It is also found that the rate of deposition on conducting substrate was higher as compared to that on non-conducting substrate (anode). XRD studies of the calcined powder and coating exhibit an expected simple cubic perovskite structure. The deposition yield increases linearly with voltage for each deposition time for both conducting and non-conducting substrates. The coating on non-conducting porous anode heat treated at 1500°C for 2 hours was dense and well adherent to the anode substrate. A film thickness of about 13 μm was obtained at 70V. Such dense BZCY electrolyte coating on BZCY+NiO anode (Half cell) could be well utilized for fabrication of proton conducting SOFC single cell by applying suitable cathode layer on electrolyte film.     

Fast codebook searching in a SOM-based vector quantizer for image compression

We propose a novel method for fast codebook searching in self-organizing map (SOM)-generated codebooks. This method performs a non-exhaustive search of the codebook to find a good match for an input vector. While performing an exhaustive search in a large codebook with high dimensional vectors, the encoder faces a significant computational barrier. Due to its topology preservation property, SOM holds a good promise of being utilized for fast codebook searching. This aspect of SOM remained largely unexploited till date. In this paper we first develop two separate strategies for fast codebook searching by exploiting the properties of SOM and then combine these strategies to develop the proposed method for improved overall performance. Though the method is general enough to be applied for any kind of signal domain, in the present paper we demonstrate its efficacy with spatial vector quantization of gray-scale images.     

Relating Residual Stress and Substructural Evolution During Tensile Deformation of an Aluminum-Manganese Alloy

Interrupted tensile tests were coupled with ex situ measurements of residual stress and microtexture. The residual stress quantification involved measurements of six independent Laue spots and conversion of the interplanar spacings to the residual stress tensor. A clear orientation-dependent residual stress evolution emerged from the experiments and the numerical simulations. For the orientations undergoing negligible changes in ρ _GND (density of geometrically necessary dislocation), the residual stress developments appeared to be governed by the elastic stiffness of the grain clusters. For the others, the evolution of the residual stress and ρ _GND exhibited a clear orientation-dependent scaling.     

Submerged vs. Solid-State Conversion of Soybean Meal into a High Protein Feed Using Aureobasidium pullulans

The goal of this study was to assess the performance of Aureobasidium pullulans in converting soybean meal (SBM) into high protein feed using the submerged and solid-state processes. High solid loading rates (SLR) were evaluated for each process, i.e., 10–25% for submerged and 35–70% for static solid-state fermentation. During the submerged fermentation, 10% SLR was considered the best performer due to the high amount of cell density, low residual carbohydrates, and high protein titers, while 40% SLR resulted in the high protein yields and low residual carbohydrates during the static solid-state fermentation. The solid-state fermentation was conducted in a 14-L paddle-type reactor at 50% SLR, and periodic mixing resulted in a protein titer of ~58% at 72 hours of fermentation. Overall, results showed the feasibility of scaling up these processes in converting SBM to a high protein feed ingredient for animal diet.