the purpose of this paper is to use discrete event simulation to model breakdowns of filling machines for food manufacturing line. It presents an approach to model breakdowns based on purely historical data where MTBF and MTTR have been extracted in order to replicate the existing machine and the breakdown occurrences. The historical data gathered from the operations department were first analysis over a period of 28 months in order to extract the Failure and repair distributions of machines; these statistical distributions were applied in the first model.

The data were also used to calculate the components of the OEE of the filling line to identify potential areas of improvement.

The models were built using Arena Simulation Package; the simulation used to represent the filling line machines with a series of elements and built in templates in addition to animation tools to improve the display at the human interface. Simulation enables the replicating of the business model in a computer system totally risk free, as you can apply and make changes to the model, create “what if” scenarios to test strategic changes, and extract results.

• Experimental and theoretical comparisons have been performed for natural convection heat transfer over rectangular fins array at different fin parameters. This investigation includes the effect of fin length, fin spacing, fin height, orientation angle, and temperature difference between the heat sink and the surrounding environment. To understand the general flow patterns dominating flows from the heat sink, the three dimensionless elliptic governing equations were solved using finite volume computational fluid dynamics (CFD) code, and the experimental work was carried for the system at different orientations. A new empirical correlation (modified of McAdam's correlation) was derived to correlate the mean Nusselt number as a function of the Rayleigh number. The average heat transfer coefficient has a maximum value at an orientation angle equal to zero degrees, and it decreases with an increasing orientation angle. The heat transfer rate per unit base area increases as fin spacing increase until it reaches a maximum value (6.5 mm), then it decreases with a further increase of fin spacing. The results of these investigations between the experimental and theoretical study were showing good agreements with similar international works.

Experimental and numerical investigations have been performed to study the natural convection heat transfer from a vertical rectangular fin arrays at different orientation angles.An experimental setup was constructed and calibrated to test different fin configurations. It basically consists of base plate, an array of parallel longitudinal fins, heating unit and layers of thermal insulation. Fin length (L) and fin thickness (t) were kept fixed at 187 and 6.5 mm respectively, while fin spacing (S) was varied from 3 to 16 mm and fin height (H) was varied from 15 to 45 mm. The orientation angle (β) was changed from 0° to 60°, and temperature difference between fin and surrounding (∆T) from 30 to 95 o C.Base-to-ambient temperature difference was also varied through a calibrated wattmeter ranging from 10 to 180W. To understand the general flow patterns dominating flows from the heat sink, the three-dimensionless elliptic governing equations were solved using finite volume computational fluid dynamics (CFD) code. A comparative study between the experimental and numerical results was performed to verify the numerical code. It was found for the configuration tested that the heat transfer rate per unit base area increases with the increase in the fin spacing and reaches a maximum value then decreases with farther increase in the fin spacing. The maximum heat dissipation occurs at optimal spacing S opt =7 mm. Empirical correlations between Nussult number, Rayleigh number, fin spacing, fin height, orientation angle, temperature difference between the fin and surroundings were derived. Finally the present work general empirical formula is given in the form =. .. .. Where , 15 mm ≤ H ≤ 45 mm, 3mm ≤ S ≤ 16 mm, °0 ≤ β ≤°60, t = 6.5 mm, L = 187 mm.

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Traditional building energy audits are both expensive, in the range of USD $1.29/m 2-$5.37/m 2, and inconsistent in their prediction of potential energy savings. Automation to reduce costs of evaluating the energy effectiveness of buildings is strongly needed. A key element of such automation is a means to estimate the building envelope energy effectiveness. We present a method that addresses this need by using infrared thermography to characterize building wall envelope effectiveness. To date, thermal imaging approaches for estimating wall R-Values, based upon thermal-physical models of walls, require additional manual measurements and analysis which prohibit low-cost, large-scale implementation. To overcome this implementation challenge, a machine learning approach is used to predict wall R-Values for a set of residences with known thermal resistance by utilizing the measured wall imaging temperature, prior weather conditions, historical energy consumption data, and available building geometrical data. The developed model is shown to predict wall R-Values with a maximum test-set root mean squared error of 7% using as few as nine training houses. This result has significant implications for low-cost large-scale envelope energy effectiveness characterization.

Cooling accounts for 12-38% of total energy consumption in schools in the US, depending on the region. In this study, stacking learning is utilized to predict chiller running capacity for four school buildings (regression) and to predict the chiller status for four another schools (classification) using a collection of interval chiller data and building demand. Singular and multiple measurement periods within one or more seasons are considered. A generalized methodology for modeling building energy systems is posited that informs selection of features, data balancing to attain the best model possible, ensemble-based stacked learning in order to prevent over-fitting, and final model development based upon the results from the stacked learning. The results show that ensemble-based stacked learning improves the model performance substantially; providing the most accurate results for both regression and classification. for both classification and regression. For, classification, the balanced accuracy is 99.79% while Kappa is 99.39%. For regression, the R-squared value, the mean absolute error (MAE) error, and the root mean squared error (RMSE) are 1.78 kW, 2.77 kW, and 0.983 respectively.

Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical and energy characteristics. Most importantly, unique power spectra are developed for over 100 individual residences from the measured thermostat indoor temperature in each and used as a predictor in the training of a singular machine learning models to predict consumption in any residence. The best model yielded a percentage mean absolute error (MAE) for monthly gas consumption ±8.6%. Applied to two residences to which attic insulation was added, the resolvable energy savings percentage is shown to be approximately 5% for any residence, representing an improvement in the ASHRAE recommended approach for estimating savings from whole-building energy consumption that is deemed incapable at best of resolving savings less than 10% of total consumption. The approach posited thus offers value to utility-wide energy savings measurement and verification.

Design parameters are presented for Tajoura reactor core utilizing the new fuel assemblies with low enriched uranium (LEU, using IRT-4M fuel assemblies) in the steady state safety operational parameters and Loss of Flow transient mathematical models (NATIRT - computer program. The calculated results of the model are presented in the cases of forced convection steady state, transient during emergency tank filling and natural convection after emergency tank filling modes at different reactor core thermal power level. The results of NATIRT for all cases of flow were in good agreement with the PARET and PLTEMP computer programs.