Optima localization by vehicle formations imitating the Nelder-Mead simplex algorithm

In this paper, we address the problem of localizing extrema points and iso-contours of ambient environmental fields (specifically, ocean bottom landscape and underwater plumes) using a networked formation of autonomous underwater vehicles. We propose the use of the Nelder-Mead extension to the basic simplex nonlinear optimization algorithm. In these robust gradient-free strategies, decisions are solely made based on field values measured by the individual vehicles, while measurements are fused and actions decided according to the algorithm. A main goal of this paper is to trigger interest in direct search methods as pertains to this type of robotic problem.     

THERMODYNAMICS OF MOISTURE SORPTION IN ALFALFA PELLETS

ABSTRACT

Experimental data on sorption isotherms of alfalfa pellets were used to determine the thermodynamic functions (differential heat of sorption, spreading pressure, net integral enthalpy and entropy) of the pellets. The thermodynamic functions were then utilized in modeling the specific heats of pellets as a function of moisture content. The estimated values of the thermodynamic functions at different moisture contents confirmed the results of an earlier study on the influence of moisture sorption on the physical integrity of the pellets. The changes in the thermodynamic properties and physical integrity were prominent when the moisture of the pellets were between 0.10 and 0.12 (mass fraction basis). Using the differential heat of sorption, it was determined that alfalfa pellets have higher affinity for moisture uptake when compared to wheat and lentils. The specific heat of the solids of alfalfa pellets had a parabolic relationship with moisture whereas the specific heat of water fraction increased linearly with moisture content of the pellets.     

Size reduction of high- and low-moisture corn stalks by linear knife grid system

High- and low-moisture corn stalks were tested using a linear knife grid size reduction device developed for first-stage size reduction. The device was used in conjunction with a universal test machine that quantified shearing stress and energy characteristics for forcing a bed of corn stalks through a grid of sharp knives. No published engineering performance data for corn stover with similar devices are available to optimize performance; however, commercial knife grid systems exist for forage size reduction. From the force–displacement data, mean and maximum ultimate shear stresses, cumulative and peak mass-based cutting energies for corn stalks, and mean new surface area-based cutting energies were determined from 4–5 refill runs at two moisture contents (78.8% and 11.3% wet basis), three knife grid spacings (25.4, 50.8, and 101.6 mm), and three bed depths (50.8, 101.6, and 152.4 mm). In general, the results indicated that peak failure load, ultimate shear stress, and cutting energy values varied directly with bed depth and inversely with knife grid spacing. Mean separation analysis established that high- and low-moisture conditions and bed depths ≥ 101.6 mm did not differ significantly (P < 0.05) for ultimate stress and cutting energy values, but knife grid spacing were significantly different. Linear knife grid cutting energy requirements for both moisture conditions of corn stalks were much smaller than reported cutting energy requirements. Ultimate shear stress and cutting energy results of this research should aid the engineering design of commercial scale linear knife gird size reduction equipment for various biomass feedstocks.     

Application of a Model to Simulate the Wetting and Drying Processes of Woody Biomass in the Field

The variation of moisture content in the biomass materials would affect the quality during the utilization of these materials as solid biofuel. The ability to predict the time-dependent moisture contents of the biomass via modeling can help to devise a better way to store and manage these biomass materials. In this study, pieces of aspen stems were subject to cycles of wetting and drying in lab-scale tests. A lumped mathematical model for simulating the moisture changes during storage was developed and calibrated using the experimental data. With the available weather data (air temperature, relative humidity, solar radiation, wind speed, and precipitation) as inputs, the model was then applied to estimate the moisture content of aspen (Populus tremuloides) during one year of storage in the field. Results showed that, for both uncovered bales and covered bales, the predicted moisture contents and the profiles were in good agreement with the measured in-field results. This lumped model may be used as a first approximation, and applied to estimate the moisture content of aspen or similar woody biomass materials during relatively long-term field storage.     

Comparison of essential oils compositions of Nepeta persica obtained by supercritical carbon dioxide extraction and steam distillation methods

Essential oil of Nepeta persica cultivated in Iran was obtained by steam distillation and supercritical (carbon dioxide) extraction methods. The oils were analysed by capillary gas chromatography using flame ionization and mass spectrometric detections. The compounds were identified according to their retention indices and mass spectra (EI, 70 eV). The effects of different parameters such as pressure, temperature, modifier volume and extraction times (dynamic and static) on the supercritical fluid extraction (SFE) of N. persica oil were investigated. The results showed that under the pressure of 20.3 MPa, temperature of 45 °C, methanol of 1.5% v/v), dynamic extraction time of 50 min and static extraction time of 25 min extraction was more selective for the 4αβ,7α,7aα-nepetalactone. Twelve compounds were identified in the steam-distilled oil. The major components of N. persica were 4αβ,7α,7aα-nepetalactone (26.5%), cis-β-farnesene (4.4%) and 3,4α-dihydro-4aα,7α,7aα-nepetalactone (3.5%). However, by using supercritical carbon dioxide under optimum conditions, only two components have more than 90.0% of the oil. The extraction yield based on steam distillation was 0.08% (v/w). On the other hand, using SFE extraction yield in the range of 0.22–8.90% (w/w) were obtained at different conditions. The results show that, in Iranian N. persica oil, 4αβ,7α,7aα-nepetalactone is a major component.     

HEAT TREATMENT OF CHOPPED ALFALFA IN ROTARY DRUM DRYERS

This paper deals with the heat treatment of alfalfa chops during the high temperature dehydration process. It outlines the dryer characteristics, difficulties and potential errors in measuring temperatures in the dryer, computation techniques, the relationships between moisture and temperature during drying, and the potential effect of dehydration on the destruction of the insect Hessian Fly due to elevated temperatures.

From the analysis based on an existing computer model for dehydration of alfalfa chops, and the available field data, it is shown that the dried chops will attain a temperature of 90°C or higher when the input temperatures are between 500°C and 800°C. These conditions apply to the drying of wet alfalfa (moisture content more than 55 percent wet basis). The plant material loses a large portion of its moisture in the first few seconds in the dryer. The rapid release of moisture may cause the rupture or detachment of particles such as eggs, larva, pupa, and insects from the plant material. These small particles are exposed to an intense heat and rapid dehydration.     

DYNAMIC SHRINKAGE AND VARIABLE PARAMETERS IN BAKKER-ARKEMA'S MATHEMATICAL SIMULATION OF WHEAT AND CANOLA DRYING

The bulk volume shrinkage was linearly related to instantaneous grain moisture reduction. The shrinkage coefficient was found to be primarily a function of air relative humidity. The Bakker-Arkema's bulk grain drying model was modified to incorporate the shrinkage equation. A correction factor for thin layer drying equation was also introduced to reduce deviation between the experimental and simulated moisture content. The Bakker-Arkema model showed significant improvement due to these modifications.     

A new methodology for the identification of best practices in the oil and gas industry, using intelligent systems

In this paper the theory and methodology of “Intelligent Best Practices Analysis” is presented. The methodology is then applied to a database of stimulation practices in the Golden Trend fields of Oklahoma to demonstrate its use and benefits.In the Golden Trend fields of Oklahoma, like many other oil fields, the outcome of stimulation jobs have not been the same for all wells. The effectiveness of the stimulation is a function of several factors including reservoir quality, completion and stimulation practices. Completion and stimulation practices can be further itemized as completion type such as open hole versus cased hole with perforations and the type and amount of fluids and proppants that is used during the stimulation and the rate at which they are pumped into the formation.Detail stimulation data from more than 230 wells in the Golden Trend operated by three independent operators were collected and analyzed using this methodology. The study was performed both for gas and oil bearing formations. The Best Practices Analysis pointed out that in the carbonate formations of the Golden Trend that are primarily gas producing, acid fracs are much more effective than acid jobs (where no proppant is pumped into the formation). For the clastic formations in the Golden Trend, from which both oil and gas are produced, the Best Practices Analysis showed that most effective fluids were those with a diesel oil base. Furthermore the analysis concluded that the formations in the Golden Trend respond best to frac jobs with proppant concentration of 1 lb/mgal/ft or higher that are injected at rates up to 1 gal/min/ft.     

Simultaneous enantioseparation and tandem UV-MS detection of eight beta-blockers in micellar electrokinetic chromatography using a chiral molecular micelle

The feasibility of using a new and more versatile polymeric chiral surfactant, i.e., poly(sodium N-undecenoxy carbonyl-L-leucinate (poly-L-SUCL) is investigated for simultaneous enantioseparation and detection of eight structurally similar beta-blockers with tandem UV and MS detection. Three optimization approaches, i.e., direct infusion-MS, capillary zone electrophoresis-MS, and chiral micellar electrokinetic chromatography-mass spectrometry (CMEKC-MS), were investigated to optimize sheath liquid parameters, spray chamber parameters, and CMEKC separation parameters for maximum sensitivity and chiral resolution. Compared to unpolymerized micelle of L-SUCL, the use of micelle polymer (i.e., poly-L-SUCL) provided significantly higher separation efficiency, lower separation current, and higher detection sensitivity for CMEKC-ESI-MS of beta-blockers. It was also observed that, unlike monomeric L-SUCL, polymeric L-SUCL provided enantioseparation of all beta-blockers even at the lowest surfactant concentration (i.e., 5 mM poly-L-SUCL). Under optimum CMEKC and ESI-MS conditions (15 mM poly-L-SUCL, 25 mM each of NH4OAc and TEA (pH 8.0); 80% (v/v) methanol sheath liquid containing 40 mM NH4OAc (pH 8.0); sheath liquid flow rate, 5.0 microL/min; drying gas flow rate, 5 L/min; drying gas temperature, 200 degrees C; nebulizing pressure, 6 psi (0.414 bar); capillary voltage, +2.5 kV; fragmentor voltage, 85 V), baseline enantioseparation of eight beta-blockers was achieved by tandem UV (in approximately 30 min) and MS (in approximately 60 min) detection. Calibration curves for all beta-blockers were linear in the range of 0.01-0.6 mM for both CMEKC-UV and CMEKC-MS methods, but the later method provided better concentration limit of detection with similar RSD for migration time and peak areas. The CMEKC-ESI-MS method appears suitable for use as a routine procedure for high-throughput separation of beta-blockers with high sensitivity.     

Storage Properties of Low Fat Fish and Rice Flour Coextrudates

In the present study, response surface method (RSM) and genetic algorithm (GA) were used to study the effects of process variables like screw speed, rpm (x ₁), L/D ratio (x ₂), barrel temperature (°C; x ₃), and feed mix moisture content (%; x ₄), on flow rate of biomass during single-screw extrusion cooking. A second-order regression equation was developed for flow rate in terms of the process variables. The significance of the process variables based on Pareto chart indicated that screw speed and feed mix moisture content had the most influence followed by L/D ratio and barrel temperature on the flow rate. RSM analysis indicated that a screw speed > 80 rpm, L/D ratio > 12, barrel temperature > 80 °C, and feed mix moisture content > 20% resulted in maximum flow rate. Increase in screw speed and L/D ratio increased the drag flow and also the path of traverse of the feed mix inside the extruder resulting in more shear. The presence of lipids of about 35% in the biomass feed mix might have induced a lubrication effect and has significantly influenced the flow rate. The second-order regression equations were further used as the objective function for optimization using genetic algorithm. A population of 100 and iterations of 100 have successfully led to convergence the optimum. The maximum and minimum flow rates obtained using GA were 13.19 × 10⁻⁷ m³/s (x ₁ = 139.08 rpm, x ₂ = 15.90, x ₃ = 99.56 °C, and x ₄ = 59.72%) and 0.53 × 10⁻⁷ m³/s (x ₁ = 59.65 rpm, x ₂ = 11.93, x ₃ = 68.98 °C, and x ₄ = 20.04%).