Development Of Empirical Model Research Articles

Development and application of empirical models to optimize the control of an internal combustion engine

Introduction. The quality of the engine control units software (SW) significantly determines the output performance of the internal combustion engine (ICE). An important component of the software development process is its adaptation or calibration, which includes a large amount of work to select specific values of control actions to improve the performance of the internal combustion engine operating cycle under toxicity restrictions. The purpose of the work was to reduce time and material costs during the initial calibration work. Methodology and research methods . To achieve the goal the application of the calibration technique to the gasoline engine control system was proposed which was to be carried out by using empirical (obtained experimentally) ICE models. The main tool used in the work was the ASCMO software, which was used with ETAS technical support. The calibration process presented in the article was divided into stages: development of an experiment plan, testing (carried out on a gasoline engine model), analysis and processing of the results with the construction of an empirical ICE model, optimization of controlled influences and preparation of calibration maps taking into account the restrictions of harmful emissions during car cycle testing. Scientific novelty and results . The technique of initial ICE calibration based on the use of an empirical engine model has been formulated. The presented results were obtained without taking into account the statutory norms and rules, the imposed restrictions were formed in an arbitrary way. Practical significance . The presented method is of practical importance, since it allows to optimize labor costs for carrying out calibration work. A brief assessment of the effectiveness and applications of the technique is provided in the publication.

Development of empirical models for analysis of subsoil agricultural parameters from resistivity measurement in a basement complex terrain

ABSTRACT Soil electrical resistivity measurements and soil properties determination were carried out at an arable plot located within the Federal University of Technology, Akure, Ondo State. This was with a view to establishing relationship between in-situ soil resistivity and selected topsoil properties in a typical basement complex environment. Electrical Resistivity was measured from the soil surface at 0 to 50 cm soil depths using Wenner Array. Electrode spacing of 130 cm (1.3 m) was utilized. Soil samples were collected to a depth of 50 cm at the mid points of 2 m by 2 m cell. These were analyzed for properties that include: moisture content, particle size analysis, Organic Matter (OM), pH and electrical conductivity. The regression analysis plots show that ER correlate significantly to soil properties, with coefficient of correlation of 0.90 for MC, 0.63 for SC, 0.74 for CC, 0.57 for OM, 0.94 for EC. Except for SC and PH that shows a non-significant correlation of 0.52 and 0.30 respectively. Validation of the derived empirical model gives a coefficient of correlation between the observed and predicted result as 0.94 for MC, 0.74 for OM, 0.93 for EC, 0.79 for SC, 0.94 for silt content, 0.52 for clay content and 0.48 for PH. The study concluded that electrical resistivity measurements could be used as a rapid tool for obtaining the selected topsoil properties.

Development of Empirical Bioavailability Models for Metals.

Recently, there has been renewed interest in the development and use of empirical models to predict metal bioavailability and derive protective values for aquatic life. However, there is considerable variability in the conceptual and statistical approaches with which these models have been developed. In the present study, we review case studies of empirical bioavailability model development, evaluating and making recommendations on key issues, including species selection, identifying toxicity-modifying factors (TMFs) and the appropriate environmental range of these factors, use of existing toxicity data sets and experimental design for developing new data sets, statistical considerations in deriving species-specific and pooled bioavailability models, and normalization of species sensitivity distributions using these models. We recommend that TMFs be identified from a combination of available chemical speciation and toxicity data and statistical evaluations of their relationships to toxicity. Experimental designs for new toxicity data must be sufficiently robust to detect nonlinear responses to TMFs and should encompass a large fraction (e.g., 90%) of the TMF range. Model development should involve a rigorous use of both visual plotting and statistical techniques to evaluate data fit. When data allow, we recommend using a simple linear model structure and developing pooled models rather than retaining multiple taxa-specific models. We conclude that empirical bioavailability models often have similar predictive capabilities compared to mechanistic models and can provide a relatively simple, transparent tool for predicting the effects of TMFs on metal bioavailability to achieve desired environmental management goals. Environ Toxicol Chem 2019;39:85-100. © 2019 SETAC.

Quantitative Acoustic Models for Superfluid Circuits.

We experimentally realize a highly tunable superfluid oscillator circuit in a quantum gas of ultracold atoms and develop and verify a simple lumped-element description of this circuit. At low oscillator currents, we demonstrate that the circuit is accurately described as a Helmholtz resonator, a fundamental element of acoustic circuits. At larger currents, the breakdown of the Helmholtz regime is heralded by a turbulent shedding of vortices and density waves. Although a simple phase-slip model offers qualitative insights into the circuit's resistive behavior, our results indicate deviations from the phase-slip model. A full understanding of the dissipation in superfluid circuits will thus require the development of empirical models of the turbulent dynamics in this system, as have been developed for classical acoustic systems.

National-scale development and calibration of empirical models for predicting daily global solar radiation in China

Accurate global solar radiation (Rs) information is pivotal to the design and management of solar energy systems. Nevertheless, the expensive devices for Rs measurements make Rs data always unavailable in many regions around the world. The empirical models that predict Rs using other widely available climatic variables are feasible alternatives when Rs measurements are unavailable. However, the parameters of empirical models are site-specific and always need local calibration. In this context, the present study firstly developed a novel empirical model for accurately predicting Rs at the national scale in China, and then compared the new model with nineteen locally calibrated empirical models that have been largely reported in prior studies, including seven sunshine-based, nine temperature-based, and three complex empirical models. Daily Rs and other meteorological data during 1994–2016 from 96 weather stations in China were used to calibrate/develop and assess the models. The results showed that the newly proposed C4 model generally offered the best prediction accuracy among the models, with average MAE of 1.69 MJ m−2 d−1, RRMSE of 16.2% and NS of 0.895, which can be recommended as the optimal model for predicting Rs in China. The models reviewed and developed in this study improved the prediction accuracy of Rs, which can provide crucial information for the design and implementation of solar photovoltaic and thermal systems.

Up-scaling mercury emissions from terrestrial surfaces as a response to sustained temperature increase

It has been well established that human activities have significantly altered the earth's climate system. Terrestrial Mercury (Hg) emissions exhibit a strong relationship with meteorological variables, in particular solar radiation and temperature. Alterations to these controlling parameters resulting from climate change will influence Hg air-surface exchange trends. Over the past two decades, using the same micrometeorological-based methodology, mercury flux data was collected at a number of varying terrestrial sites globally. This study aimed to investigate the relationship between climate variables and mercury air-surface exchange. The large data set identified significant relationships between Hg air-surface exchange and temperature that was consistent across the multiple field sites, facilitating the development of a well-constrained empirical model to predict the impact of climate change on terrestrial mercury air-surface exchange, with changing temperature. Flux increases calculated, based on IPCC Temperature projections, ranged between 15 and 43% increase for a 1–2 °C rise in temperature by 2050 and 15–96% increase for temperature rise between 1 and 3.7 °C by 2100. The projections developed here indicate that air temperature can be used as a baseline for determining potential terrestrial evasion under projected climate change, prolonging the recovery time of the natural mercury cycle in view of reduced anthropogenic emissions.

Empirical model development for the estimation of clearness index usingmeteorological parameters

The clearness index is an indispensable parameter required for the design and analysis of solar energy systems. In the absence of measured values for a specific location, the clearness index can be estimated from other measured meteorological variables. In this study three meteorological parameters, sunshine hours, monthly mean values of the temperature difference ($\Delta$T), and cloudiness, are used to develop empirical models for the estimation of clearness index. The empirical models are developed for five major cities in Pakistan (Karachi, Multan, Lahore, Islamabad, and Quetta). For empirical model development, long-term data (1991 to 2010) of monthly average clearness index, sunshine hours, average daily minimum and maximum temperatures, and cloudiness have been used. The accuracy of the models has been tested by statistical indicators that include mean percentage error (MPE), coefficient of determination (R$^2$), mean absolute relative error (MARE), mean bias error (MBE), and root mean square error (RMSE). The error analysis revealed that the proposed models are suitable for the estimation of the clearness index. It is also concluded that multiple regression models give better estimates of clearness index for all the stations (0.80 $\leq$ R$^{2}$ $\leq$ 0.86) compared to single parameter model and therefore are recommended. The study indicated that clear sky conditions prevail throughout the months at all the investigated sites (0.58 $\leq$ K$_T$ $\leq$ 0.68), which is a good indicator for solar energy utilization. The statistical indicators also suggest that multilinear regression model M-3 gives a better representation of the climate system and using three parameters reduces the uncertainties in the developed model.

Optimization of castor and neem biodiesel blends and development of empirical models to predicts its characteristics

The main objective of the present study is to make different blends of neem and castor biodiesel in order to eliminate the deficiency of the higher cloud point and pour point in case of neem biodiesel and higher kinematic viscosity and density in case of castor biodiesel. The extracted castor and neem seed oil were first converted into biodiesel through transesterification. Then raw biodiesel and their blends were analyzed for their kinematic viscosity, density, specific gravity, flash point, fire point and cloud point and pour point as per standard methods. As per results, based on the ASTM biodiesel standard, the blend of biodiesel containing 50% of each constituent was found optimum. The kinematic viscosity, density, cloud and pour point of the optimum blend were 5.83 cSt, 896 kg/m3, 5 °C, −4°C, respectively. Moreover, an empirical model was also developed to predict the characteristics of castor and neem biodiesel blends as the function of the blend ratio.

Modelling of Level Process

Industries such as, textile, food processing, chemical and water treatment plants are part of our global development. The efficiency of processes used by them is always a matter of great importance. Efficiency can be greatly improved by obtaining an exact model of the process. This paper studies the two main classifications of model development – First-Principles Model and Empirical Model. First-Principles Model can be obtained with an understanding of the basic physics of the system. On the other hand, Empirical Models require only the input-output data and can thus factor in process non-linearity, disturbances and unexpected errors.This paper utilizes the System Identification Toolbox in MATLAB for empirical model development. Models are developed for a single tank system, a classic SISO problem and for the two interacting tank system. Both systems are studied with respect to three operating points, each from a local linear region. The obtained models are validated with the real-time setup. They are satisfactory in their closeness to the real time process and hence deemed fit for use in control algorithms and other process manipulations.

Leaf Wetness Duration Models Using Advanced Machine Learning Algorithms: Application to Farms in Gyeonggi Province, South Korea

Leaf wetness duration (LWD) models have been proposed as an alternative to in situ LWD measurement, as they can predict leaf wetness using physical mechanism and empirical relationship with meteorological conditions. Applications of advanced machine learning (ML) algorithms in the development of empirical LWD model can lead to improvements in the LWD prediction. The current study developed LWD model using extreme learning machine, random forest method, and a deep neural network. Additionally, performances of these ML-based LWD models are evaluated and compared with existing models. Observed LWD and meteorological variable data are obtained from nine farms in South Korea. Temporal and geographical information were also used. Additionally, the priorities of the employed variables in the development of the ML-based LWD models were analyzed. As a result, the ML-based LWD models outperformed the existing models; the random forest led to the best performance for LWD prediction among the tested LWD models. Strengths of associations between input variables and leaf wetness were relative humidity, short wave radiation, air temperature, hour, latitude, longitude, and wind speed in descending order. Uses of the geographical and time information in development of LWD model can improve the performance of LWD model.

Development of empirical models to predict cooling performance of a thermoelectric radiant panel

Performance simulations of thermoelectric radiant panels (TERPs) provide valuable information for the use of TERPs as alternatives to conventional hydraulic radiant panels, which require refrigerants based on the vapor compression cycle. Several existing models are appropriate for the design of the TERP; however, there are limitations to the building simulation with respect to the evaluation of the annual energy performance because existing models for TERP are based on the numerical method, therefore it is very complex to integrate with the building model. To overcome these limitations, simplified empirical models should be developed based on experimental data and then verified.This study aims to develop two simplified prediction models for the cooling capacity and energy consumption of TERPs. Using the 2k factorial experiment design method, a total of 16 experiment sets were designed according to the four main operation parameters: room temperature, outdoor air temperature, face velocity of air in duct, and combined heat transfer coefficient between room and panel surface. The developed models were designed in dimensionless forms to ensure broad applicability. The developed models yielded good R2 values exceeding 0.99 and were validated under additional operation conditions. Finally, the guidance of using the developed models and the performance tables of the TERPs was confirmed with respect to the use of TERPs in the construction of heating, ventilation, and air conditioning (HVAC) systems.

Clonal Reconstruction of Thyroid Cancer: An Essential Strategy for Preventing Resistance to Ultra-Precision Therapy.

The introduction of ultra-precision targeted therapy has become a significant advancement in cancer therapeutics by creating treatments with less off target effects. Specifically with papillary thyroid carcinoma (PTC), the cancer's hallmark genetic mutation BRAFV600E can be targeted with selective inhibitors, such as vemurafenib. Despite initial positive tumor responses of regression and decreased viability, both single agent or combination agent drug treatments provide a selective pressure for drug resistant evolving clones within the overall heterogeneous tumor. Also, there are evidences suggesting that sequential monotherapy is ineffective and selects for resistant and ultimately lethal tumor clones. Reconstructing both clonal and subclonal thyroid tumor heterogeneous cell clusters for somatic mutations and epigenetic profile, copy number variation, cytogenetic alterations, and non-coding RNA expression becomes increasingly critical as different clonal enrichments implicate how the tumor may respond to drug treatment and dictate its invasive, metastatic, and progressive abilities, and predict prognosis. Therefore, development of novel preclinical and clinical empirical models supported by mathematical assessment will be the tools required for estimating the parameters of clonal and subclonal evolution, and unraveling the dormant vs. non-dormant state of thyroid cancer. In sum, novel experimental models performing the reconstruction both pre- and post-drug treatment of the thyroid tumor will enhance our understanding of clonal and sub-clonal reconstruction and tumor evolution exposed to treatments during ultra-precision targeted therapies. This approach will improve drug development strategies in thyroid oncology and identification of disease-specific biomarkers.

The implications of Simpson's paradox for cross-scale inference among lakes

Using cross-sectional data for making ecological inference started as a practical means of pooling data to enable meaningful empirical model development. For example, limnologists routinely use sample averages from numerous individual lakes to examine patterns across lakes. The basic assumption behind the use of cross-lake data is often that responses within and across lakes are identical. As data from multiple study units across a wide spatiotemporal scale are increasingly accessible for researchers, an assessment of this assumption is now feasible. In this study, we demonstrate that this assumption is usually unjustified, due largely to a statistical phenomenon known as the Simpson's paradox. Through comparisons of a commonly used empirical model of the effect of nutrients on algal growth developed using several data sets, we discuss the cognitive importance of distinguishing factors affecting lake eutrophication operating at different spatial and temporal scales. Our study proposes the use of the Bayesian hierarchical modeling approach to properly structure the data analysis when data from multiple lakes are employed.

Halogen Bond Structure and Dynamics from Molecular Simulations.

Halogen bonding has emerged as an important noncovalent interaction in a myriad of applications, including drug design, supramolecular assembly, and catalysis. The current understanding of the halogen bond is informed by electronic structure calculations on isolated molecules and/or crystal structures that are not readily transferable to liquids and disordered phases. To address this issue, we present a first-principles simulation-based approach for quantifying halogen bonds in molecular systems rooted in an understanding of nuclei-nuclei and electron-nuclei spatial correlations. We then demonstrate how this approach can be used to quantify the structure and dynamics of halogen bonds in condensed phases, using solid and liquid molecular chlorine as prototypical examples with high concentrations of halogen bonds. We close with a discussion of how the knowledge generated by our first-principles approach may inform the development of classical empirical models, with a consistent representation of halogen bonding.

Predictive models and detection methods applicable in water detection framework for industrial electric arc furnaces

This paper introduces the development of empirical predictive models and detection methods that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The predictive models investigated in this work are designed based on different techniques such as statistical fingerprinting, artificial neural network (ANN), and multiway projection to latent structures (MPLS). Robustness issues related to each method are discussed and performance comparisons have been done for the presented techniques. Furthermore, model fusion theory has been applied to improve the prediction accuracy of the developed models’ defined output- the value of off-gas water vapor- which is known as one the most vital variables to guarantee a safe and reliable operation. Finally based on the proposed predictive models, a water leak detection methodology is introduced and implemented on an industrial AC EAF and a comprehensive discussion has been done to evaluate the performance of the developed algorithm. To this aim, two fault detection methods have been applied. Fault detection method #1 has been designed using statistical fingerprinting technique, while the other one has been developed based on machine learning-based models and also fusion of the models’ outputs.

Modelling of scroll expander for different working fluids for low capacity power generation

This paper presents the experimental investigation and development of a general semi empirical scroll expander model for different working fluids. Initially, the characterization and modelling of a scroll expander with R134a working fluid is performed. The influence of key operating variables on the main performance indicators such as isentropic efficiency, filling factor and specific power are investigated. The proposed semi empirical model for the scroll expander predicts mass flow rate, mechanical shaft power and exhaust temperature at accuracies of 3%, 9% and 2 K respectively. The developed semi empirical model is then modified to accommodate different working fluids; validation of the generalization procedure is performed with experimental data using ammonia/water mixture and ammonia as working fluids. The generalized semi-empirical model predicts mass flow rate, mechanical shaft power and exhaust temperature to be less than 5%, 7% and 4 K error margin for both ammonia/water and pure ammonia working fluids. Therefore, the simulation model presented in this study can save the time and material resources for the selected scroll expander solely as a consequence of a change in the working fluid to predict the main performance indicators.

Process optimization and empirical model development for lignocellulosic biomass via gravimetric analysis

This study was undertaken at laboratory level to investigate the effect of nitrogen on gravimetric analysis of biomass. Nineteen samples of lignocellulosic biomass were collected. After preparation of the samples, their gravimetric analysis was performed to determine the moisture content, total solids, volatile solids, fixed solids, and ash content. In the present research work, four approaches were used to determine the value of moisture content, total solids, volatile solids, fixed solids, and ash including with nitrogen without lid, with nitrogen with lid, without nitrogen without lid, and without nitrogen with lid. After the gravimetric analysis of the biomass, the empirical models were developed using the linear regression analysis based on the least square method. The empirical equations were developed, and their mean errors were determined. The developed models were compared through regression coefficient (R2), mean absolute error, mean bias error, standard error, root sum square, and Akaike information criterion. In the developed 16 equations, the results based on the error analysis were in the specified range of R2 (0.991 ± 0.01–0.995 ± 0.04), mean absolute error (4.881 ± 0.3–43.880 ± 0.3), mean bias error (0.253 ± 0.01–41.382 ± 0.1), standard error (1.178 ± 0.01–1.625 ± 0.4), root sum square (23.426 ± 0.1–1175.911 ± 0.2), and Akaike information criterion (13.829 ± 0.1–92.148 ± 0.8). Consequently, it is noteworthy that equation 38 (considering 4 factors total solids, volatile solids, fixed solids, and ash) showed the minimum root sum square and Akaike information criterion value, confirming that this equation is considered the best for gravimetric analysis under the inert atmosphere of nitrogen without placing lid on crucibles. As per the results, the nitrogen purging gives high accuracy, whereas the cost is high.

Relationship Between Environmental Sustainability and Human Development Index: A Case of Selected South Asian Nations

All the economists today have established that externalities (free-rider issues) and public goods are the leading causes of market failure, holding the utmost level of relevance of environmental economics. Pollution types can be segregated physically based on the channels (water, air and land) or the sectors responsible for causing it (e.g., industry, electricity generation, transport, agricultural waste disposal, etc.). Thus, sustainability emerges as a key challenge of the twenty-first century for public think tanks and business communities in the form of carbon emissions and global warming. So, in order to understand the role of sustainability in the era of development, the overall broader purpose of the current study is to study the quantitative linkages between human development index (HDI) (a sustainability measure of standard of living, i.e., per capita GDP) and environmental performance index (EPI, measure of environmental health and ecosystem vitality) for the selected South Asian nations (SANs) and comparative analysis of India with the selected developing nations from 2002 to 2016. To test whether the degree of economic expansion and standard of living have a systematic relationship with the level of environmental deterioration (existence of Kuznet curve hypotheses) in a country which poses a future threat to the global warming potential (GWP), the study employs dynamic panel modelling on selected SANs, followed by descriptive graphical synthesis to visualize the association for India in particular. Several other macroeconomic and capital flow variables, such as energy consumption, direct foreign investment and so on, are considered in the extended empirical model development in order to supplement the holistic review of the situation. The findings of the panel analysis discover HDI to be positively associated with EPI, depicting higher human capital accumulation leading to lower environmental damage and better environmental performance. Additionally, the results confirm the deviation from environmental Kuznet curve (EKC) hypotheses to link economic growth positively with climatic worsening (due to recent reversals). The study finds its niche to separate it from various other studies as it includes human capital accumulation in order to find its effect in the long run on sustainability indicator. The overall results suggest crucial policy implications. The combined efforts of government at local and national levels could help in infusing green technology-based infrastructure. Additionally, environment trading system (ETS) could further be promoted in developing nations, particularly in many Western developed nations, in order to have a greener sustainable future.

Development of Empirical Asperity Contact Model for Wet Friction Material

<div class="section abstract"><div class="htmlview paragraph">A wet clutch couples or decouples gear elements to alter torque paths in an automatic transmission system. During the gear shifting event, the clutch torque is directly transmitted to the output shaft. Hence, clutch torque heavily influences the dynamics of the transmission. In order to evaluate the behavior of the transmission early and efficiently, the development process increasingly relies on high-fidelity transmission system simulations with added complexity. However, a wet clutch continues to be modeled using Coulomb’s friction in a typical shift simulation. Its linear framework does not physically represent non-linear hydrodynamic effects due to the presence of oil layer during clutch engagement. To make up the lack of physics, Coulomb’s clutch model often requires extensive tuning to match actual shift behaviors. Alternatively, a squeeze film based clutch model, coupled with an asperity contact model, can be employed to represent hydrodynamic behaviors and enable the broader use of dynamic simulation models in transmission development. However, while the squeeze film model has been extensively studied over the years, the asperity contact model remains largely unexamined. In this research, the contact behaviors of the asperities are empirically characterized for a wet clutch friction material. The results are compared against the base theory of Greenwood-Williamson asperity contact model (GW model) which is commonly accepted in wet clutch modeling. The analysis shows that the key assumptions of GW model, specifically the elastic deformation of spherical asperity tip and Gaussian distribution of their heights, do not hold for clutch friction materials. A new empirical asperity contact model is developed for wet friction material based on asperity roughness characterization and microscopic contact area measurements. The empirical model provides an accurate representation of asperity behaviors in wet clutch modeling, as an alternative to the conventional GW model, for high-fidelity transmission system simulations. The modeling framework is also applicable to a broad range of friction materials used in dry clutches, brakes and other applications that are characterized with hard constituents embedded in an elastic matrix.</div></div>

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

AbstractCredit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.