scholarly journals Repair and maintenance costs of 4WD tractors and self propelled combine harvesters in Italy

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
Aldo Calcante ◽  
Luca Fontanini ◽  
Fabrizio Mazzetto
Keyword(s):  
Cost Estimation ◽  
Working Hours ◽  
The United States ◽  
Operating Conditions ◽  
List Price ◽  
Optimal Time ◽  
Model Parameters ◽  
Maintenance Costs ◽  
Combine Harvesters ◽  
Engine Power

Purchasing and maintaining tractors and operating machines are two of the most considerable costs of the agricultural sector, which includes farm equipment manufacturers, farm contractors and farms. In this context, repair and maintenance costs (R&M costs) generally constitute 10-15% of the total costs related to agricultural equipment and tend to increase with the age of the equipment; hence, an important consideration in farm management is the optimal time for equipment replacement. Classical, R&M cost estimation models, calculated as a function of accumulated working hours, are usually developed by ASAE/ASABE for the United States operating conditions. However, R&M costs are strongly influenced by farming practices, operative conditions, crop and soil type, climatic conditions, etc. which can be specific for individual countries. In this study, R&M cost model parameters were recalculated for the current Italian situation. For this purpose, data related to the R&M costs of 100 4WD tractors with engine power ranging from 59 to 198 kW, and of 20 SP combine harvesters (10 straw walkers combines and 10 axial flow combines) with engine power ranging from 159 to 368 kW working in Italy were collected. According to the model, which was obtained by interpolating the data through a two-parameter power function (proposed by ASAE/ASABE), the R&M cost incidence on the list price of Italian tractors at 12,000 working hours (estimated life of the machines) was 48.6%, as compared with 43.2% calculated through the most recent U.S. model while, for self propelled combine harvesters, the R&M cost incidence at 3,000 working hours was 23.1 % as compared with 40.2% calculated through the same U.S. model.

Correlated personality change across time and age

2021 ◽  
pp. 089020702110140
Author(s):  
Gabriel Olaru ◽  
Mathias Allemand
Keyword(s):  
Structural Equation ◽  
Rank Order ◽  
Personality Development ◽  
The United States ◽  
Change Score ◽  
Personality Change ◽  
Equation Modeling ◽  
Model Parameters ◽  
Cross Sectional ◽  
Adult Lifespan

The goal of this study was to examine differential and correlated change in personality across the adult lifespan. Studying differential and correlated change can help understand whether intraindividual trait change trajectories deviate from the norm and how these trajectories are coupled with each other. We used data from two large longitudinal panel studies from the United States that covered a total age range of 20 to 95 years on the first measurement occasion. We used correlated factor models and bivariate latent change score models to examine the rank-order stability and correlations between change across three measurement waves covering 18 years ( N = 3250) and four measurement waves covering 12 years ( N = 4145). We examined the moderation effects of continuous age on these model parameters using local structural equation modeling. The results suggest that the test–retest correlations decrease with increasing time between measurements but are unaffected by participants’ age. We found that change processes in Extraversion, Openness, Agreeableness, and Conscientiousness were strongly related, particularly in late adulthood. Correlated change patterns were highly stable across time intervals and similar to the initial cross-sectional Big Five correlations. We discuss potential mechanisms and implications for personality development research.

scholarly journals Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Gopinathan R. Abhijith ◽  
Leonid Kadinski ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Mechanistic Model ◽  
Water Distribution Systems ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Bacterial Regrowth ◽  
The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

scholarly journals Optimal Cleaning Cycle Scheduling under Uncertain Conditions: A Flexibility Analysis on Heat Exchanger Fouling

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 93
Author(s):  
Alessandro Di Pretoro ◽  
Francesco D’Iglio ◽  
Flavio Manenti
Keyword(s):  
Safety Issue ◽  
Probability Distributions ◽  
Operating Conditions ◽  
Economic Losses ◽  
Optimal Time ◽  
Model Description ◽  
Chemical Cleaning ◽  
Uncertain Variables ◽  
Cycle Scheduling ◽  
Cleaning Cycle

Fouling is a substantial economic, energy, and safety issue for all the process industry applications, heat transfer units in particular. Although this phenomenon can be mitigated, it cannot be avoided and proper cleaning cycle scheduling is the best way to deal with it. After thorough literature research about the most reliable fouling model description, cleaning procedures have been optimized by minimizing the Time Average Losses (TAL) under nominal operating conditions according to the well-established procedure. For this purpose, different cleaning actions, namely chemical and mechanical, have been accounted for. However, this procedure is strictly related to nominal operating conditions therefore perturbations, when present, could considerably compromise the process profitability due to unexpected shutdown or extraordinary maintenance operations. After a preliminary sensitivity analysis, the uncertain variables and the corresponding disturbance likelihood were estimated. Hence, cleaning cycles were rescheduled on the basis of a stochastic flexibility index for different probability distributions to show how the uncertainty characterization affects the optimal time and economic losses. A decisional algorithm was finally conceived in order to assess the best number of chemical cleaning cycles included in a cleaning supercycle. In conclusion, this study highlights how optimal scheduling is affected by external perturbations and provides an important tool to the decision-maker in order to make a more conscious design choice based on a robust multi-criteria optimization.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

scholarly journals Efficacy and Application Strategies for Propiconazole as a New Postharvest Fungicide for Managing Sour Rot and Green Mold of Citrus Fruit

Plant Disease ◽  
2012 ◽  
Vol 96 (2) ◽  
pp. 235-242 ◽  
Author(s):  
A. H. McKay ◽  
H. Förster ◽  
J. E. Adaskaveg
Keyword(s):  
Management Practices ◽  
Citrus Fruit ◽  
The United States ◽  
Optimal Time ◽  
Inoculum Concentration ◽  
Time Of Application ◽  
Sour Rot ◽  
Green Mold ◽  
Highly Effective ◽  
Moderately Resistant

Few postharvest treatments are available for managing sour rot of citrus caused by Galactomyces citri-aurantii and they are generally not very effective. The demethylation-inhibiting (DMI) triazole fungicides propiconazole and cyproconazole were found to be highly effective and more efficacious than other DMIs evaluated, such as metconazole and tebuconazole, in reducing postharvest sour rot of citrus. Additional studies were conducted with propiconazole as a postharvest treatment because it has favorable toxicological characteristics for food crop registration in the United States and the registrant supports a worldwide registration. Regression and covariance analyses were performed to determine optimal time of application after inoculation and fungicide rate. In laboratory studies, decay incidence increased when propiconazole applications were delayed from 8 to 24 h (lemon) or 18 to 42 h (grapefruit) after inoculation. Effective rates of the fungicide were 64 to 512 μg/ml and were dependent on inoculum concentration of the sour rot pathogen and on the type of citrus fruit. Propiconazole was found to be compatible with sodium hypochlorite at 100 μg/ml and 1 to 3% sodium bicarbonate without loss of efficacy for decay control on lemon. The addition of hydrogen peroxide/peroxyacetic acid at 80 μg/ml slightly decreased the effectiveness of propiconazole. Heated (48°C) solutions of propiconazole did not significantly improve the efficacy compared with solutions at 22°C. In experimental packing-line studies, aqueous in-line drenches applied alone or followed by applications of the fungicide in storage or packing fruit coatings were highly effective, reducing sour rot to between 0 and 1.2% compared with 83.8% decay incidence in the control when treatments were made up to 16 h after inoculation. When the fungicide was applied in either fruit coating, decay was only reduced to 49.1 to 57.1% incidence. Tank mixtures of propiconazole with the citrus postharvest fungicides fludioxonil and azoxystrobin were highly effective in reducing green mold caused by isolates of Penicillium digitatum sensitive or moderately resistant to imazalil and sour rot. Propiconazole will be an important postharvest fungicide for managing sour rot of citrus and potentially can be integrated into current management practices to reduce postharvest crop losses caused by DMI-sensitive isolates of P. digitatum.

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Predicting Source Water Quality Following Wildfires Using Hydrologic Modeling

2021 ◽  
Author(s):  
Katie Wampler ◽  
Kevin D. Bladon ◽  
Monireh Faramarzi
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Organic Carbon ◽  
The United States ◽  
First Year ◽  
Model Parameters ◽  
Source Water ◽  
Forested Watersheds ◽  
Field Samples ◽  
The World

<p>Forested watersheds are critical sources of the majority of the world’s drinking water. Almost one-third of the world’s largest cities and two-thirds of cities in the United States (US) rely on forested watersheds for their water supply. These forested regions are vulnerable to the increasing incidence of large and severe wildfires due to increases in regional temperatures and greater accumulation of fuels. When wildfires occur, increases in suspended sediment and organic carbon can negatively affect aquatic ecosystem health and create many costly challenges to the drinking water treatment process. These effects are often largest in the first year following a wildfire. While past research has shown the likelihood of source water impacts from wildfire, the magnitude of effects remains uncertain in most regions. In our study, we will quantify the projected short-term effects of three large (>70,000 ha) wildfires on key water quality parameters (sediment and organic carbon) in two important forested source watersheds in the Cascade Range of Oregon, US. We calibrated and validated a modified Soil and Water Assessment Tool (SWAT) to simulate streamflow, sediment loads and transport, as well as in-stream organic carbon processes for a historical period prior to wildfire. The calibrated model parameters were then modified based on literature values and burn severity maps to represent post-fire conditions of the three large wildfires. The parameter adjustments for simulating wildfire will be validated with post-fire water quality field samples from the wildfires. We will present estimations of future water quality impacts in the burned watersheds under different precipitation conditions at a daily scale for the first year following the wildfires, which will provide testable hypotheses. Additionally, we will determine catchment characteristics most critical in determining the post-fire water quality response. This work will help predict the magnitude of effects from these historic wildfires, which can inform forest and drinking water management decision making.</p>

Study On Automatic Adaptation for Control-Oriented Model of Advanced Diesel Engine

2021 ◽  
Author(s):  
Shunki Nishii ◽  
Yudai Yamasaki
Keyword(s):  
Operating Conditions ◽  
Physical Models ◽  
Model Parameters ◽  
Training Methods ◽  
Model Based Control ◽  
Practical Applications ◽  
Model Based ◽  
Automatic Adaptation ◽  
Automobile Engines ◽  
Adaptation Method

Abstract To achieve high thermal efficiency and low emission in automobile engines, advanced combustion technologies using compression autoignition of premixtures have been studied, and model-based control has attracted attention for their practical applications. Although simplified physical models have been developed for model-based control, appropriate values for their model parameters vary depending on the operating conditions, the engine driving environment, and the engine aging. Herein, we studied an onboard adaptation method of model parameters in a heat release rate (HRR) model. This method adapts the model parameters using neural networks (NNs) considering the operating conditions and can respond to the driving environment and the engine aging by training the NNs onboard. Detailed studies were conducted regarding the training methods. Furthermore, the effectiveness of this adaptation method was confirmed by evaluating the prediction accuracy of the HRR model and model-based control experiments.

Using Sliding Mode Control to Adjust Drum Level of a Boiler Unit With Time Varying Parameters

2010 ◽  
Author(s):  
Hamed Moradi ◽  
Firooz Bakhtiari-Nejad ◽  
Majid Saffar-Avval ◽  
Aria Alasty
Keyword(s):  
Water Level ◽  
Transfer Functions ◽  
Sliding Mode ◽  
Operating Conditions ◽  
Model Parameters ◽  
Feed Water ◽  
Time Varying ◽  
Boiler Unit ◽  
Varying Parameters ◽  
Time Varying Parameters

Stable control of water level of drum is of great importance for economic operation of power plant steam generator systems. In this paper, a linear model of the boiler unit with time varying parameters is used for simulation. Two transfer functions between drum water level (output variable) and feed-water and steam mass rates (input variables) are considered. Variation of model parameters may be arisen from disturbances affecting water level of drum, model uncertainties and parameter mismatch due to the variant operating conditions. To achieve a perfect tracking of the desired drum water level, two sliding mode controllers are designed separately. Results show that the designed controllers result in bounded values of control signals, satisfying the actuators constraints.

“These Guys Came Out Looking Like Movie Actors”

2016 ◽  
Vol 85 (4) ◽  
pp. 532-576
Author(s):  
Mina Roces
Keyword(s):  
World War Ii ◽  
First Generation ◽  
Working Hours ◽  
Lower Class ◽  
The United States ◽  
Manual Labor ◽  
World War ◽  
Marginal Group ◽  
Consumption Practices ◽  
Agricultural Laborer

This article analyzes the dress and consumption practices of the first generation of Filipino male migrants to the United States who arrived from 1906 until the end of World War II. It argues that Filipino migrant men used dress and consumption practices to fashion new identities that rejected their working selves as a lower-class marginal group. The contrast between the utilitarian clothes worn during working hours and the formal suit accentuated the sartorial transformation from lower-class agricultural laborer or Alaskan cannery worker to fashionable dandy and temporarily erased the stigma of manual labor. Two groups of well-dressed Filipino men behaved in contradictory ways: as binge consumers and as anti-consumers. Collectively, Filipino consumption practices that included dress challenged the parameters of social exclusion.

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