Gaming the Regulator: A Survey

Many regulators use benchmarking as part of the incentive-based regulation of natural monopolies so as to reduce the possibilities for gaming. However, the use of benchmarking can lead firms to pursue virtual rather than true performance improvements by gaming the regulator’s benchmarking in a number of ways that are contrary to the intentions of the scheme.     

Photographing impact of molten molybdenum particles in a plasma spray

Plasma-sprayed molten molybdenum particles (∼40 μm in diameter) were photographed impinging at high velocity (∼140 m/s) on a glass substrate at room temperature. An optical sensor detected thermal radiation emitted by a droplet as it approached the substrate and activated a time delay unit. After a selected time interval, an Nd:YAG laser was triggered, emitting a 5 ns pulse that provided illumination for a charge-coupled device (CCD) camera to photograph the impacting droplet through a long-range microscope. By varying the delay before pulsing the laser, different stages of droplet deformation were recorded. Impacting droplets spread into a thin circular film that ruptured and broke into small fragments. An optical detector recording thermal radiation from the impacting droplet gave a signal that increased as the droplet spread out, reached a maximum when the liquid film began to rupture, and decreased as portions of the droplet recoiled because of surface tension and then flew out of view of the photodetector.     

X-ray Photoelectron Spectroscopy of Cadmium Tin Oxide Ceramics in As-Fired and Electrochemically Reduced Forms

X-ray photoelectron spectroscopy (XPS) has been employed to investigate the chemical nature of samples of dicadmium stannate (Cd₂SnO₄) in the as-fired, electrochemically reduced, and reoxidized states. The reduction of Cd₂SnO₄ was found to be associated with a dramatic color change from bright yellow to dark green, a phenomenon commonly known as the electrochromic effect. Both quantitative XPS results and binding energy measurements proved that, upon exposure of the reduced ceramic bodies to air, the Sn²⁺ to Sn⁴⁺ transition readily took place to produce the intermediate compound, Cd₂SnO₃ with divalent tin. Prolonged exposure to the atmosphere did not result in further progress of reoxidation extending to monovalent cadmium. However, complete reoxidation of the reduced samples was possible by annealing in air at 350°C for a short period of time, e.g., 3 h, by which the original features of the as-fired state such as color and electrical conductivity were restored. The results also showed that reoxidized samples at high temperature assume the same XPS characteristics as those of as-fired ceramics.     

EVALUATION OF SURFACE FORCE-PORE FLOW AND MODIFIED SURFACE FORCE-PORE FLOW MODELS FOR REVERSE OSMOSIS TRANSPORT

The surface force-pore flow (SF-PF) model of reverse osmosis transport and the extended and modified form (the MD-SF-PF model) have been employed to predict the performance of four aromatic polyamide (FilmTec, FT30)reverse osmosis membranes. The evaluation is based on a comparison of model predictions with experimental data. Dilute sodium chloride-water solution experimental data were used to estimate model parameters. The models are then used to predict flux and separation at various operating pressures and concentrations. Membrane performance (i.e., separation and permeate flux) can be well predicted by the MD-SF-PF model while the SF-PF model predicts the performance for the sodium chloride system less satisfactorily.     

Using AR,MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data

Precipitation is one of the most important components of the hydrologic cycle as it is required for multi-objective applications including flood estimation, drought monitoring, watersheds management, hydrology, agriculture, etc. Therefore, its estimation and modeling via a suitable method is a challenging task for hydrologists. The present study seeks to model monthly precipitation at two stations located in Iran. Two artificial intelligence (AI)-based models consisting of multivariate adaptive regression splines (MARS) and k-nearest neighbors (KNN) were used as the modeling techniques. In doing so, nine single-input scenarios under limited climatic data are implemented using minimum, maximum, and mean air temperatures, dew point temperature, station pressure, vapor pressure, relative humidity, wind speed, and antecedent precipitation data. The attained results illustrate that the performance of single MARS and KNN is relatively poor when modeling the monthly precipitation. Additionally, this study develops hybrid models to enhance the precipitation modeling through combining the MARS and KNN models with three diverse types of the time series (TS) models, namely autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The most important justification for integrating the models applied is that the AI and TS-based models are respectively capable of modeling the non-linear and linear terms of the hydrological variables such as precipitation. It is therefore necessary to be considered both of the aforementioned terms in the modeling procedure. A performance comparison of the single and hybrid models denotes the higher accuracy of hybrid models than the single ones. However, the hybrid models generated by combining the KNN and the TS models used are the best-performing models.

     

Assessment of egg freshness by prediction of Haugh unit and albumen pH using an artificial neural network

Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25?±?2 °C and 20?±?3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables (p?<?0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively.     

Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations

Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson, 1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959; Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the results obtained by these methods have significant errors when compared with the experimental data. In this study a robust artificial neural network (ANN) code was developed in the MATLAB software environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999; Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with the models previously published in literature, the ANN model has a better accuracy and performance in predicting the viscosity of Iranian crudes.     

Experimental and Numerical Investigation on Free Convection From a Horizontal Cylinder Located Above an Adiabatic Surface

Steady two-dimensional free convection heat transfer from an isothermal horizontal cylinder located above an adiabatic horizontal surface is studied experimentally and numerically. Experiments are carried out using a Mach–Zehnder interferometer for the ratios of cylinder spacing from the adiabatic surface to its diameter L/D = 0, 0.1, 0.2, 0.3, 0.5, 0.7, and 0.9 and the Rayleigh number range of 500 to 15,000. Also, a specifically developed computer code based on the finite-volume method, the SIMPLE algorithm, and nonorthogonal discretization grid system is used for the solution of the mass-, momentum-, and energy-governing equations for the Rayleigh numbers ranging from 100 to 100,000 and L/D ranging from 0.1 to 1.7. The effects of the Rayleigh number and spacing from the adiabatic surface on both local and average Nusselt numbers around the cylinder are investigated. A correlation based on the numerical data for the average Nusselt number of the cylinder as a function of Rayleigh number and L/D is presented in the aforementioned ranges.