scholarly journals Total Suspended Particle Emissions Modelling in an Industrial Boiler

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3097 ◽  
Author(s):  
Guillermo Ronquillo-Lomeli ◽  
Gilberto Herrera-Ruiz ◽  
José Ríos-Moreno ◽  
Irving Ramirez-Maya ◽  
Mario Trejo-Perea
Keyword(s):  
Suspended Particle ◽  
Model Development ◽  
Least Square ◽  
Total Suspended Particle ◽  
Particulate Matter Emission ◽  
Structure Selection ◽  
Non Linear ◽  
Emissions Modelling ◽  
Basic Functions ◽  
Industrial Boiler

Particulate matter emission into the atmosphere is a massive-scale problem. Fossil fuel combustion is an important source of this kind of pollution. The knowledge of total suspended particle (TSP) emissions is the first step for TSP control. The formation of TSP emissions is poorly understood; therefore new approaches for TSP emissions source modelling are required. TSP modelling is a multi-variable non-linear problem that would only require basic information on boiler operation. This work reports the development of a non-linear model for TSP emissions estimation from an industrial boiler based on a one-layer neural network. Expansion polynomial basic functions combined with an orthogonal least-square and model structure selection approach were used for modelling. The model required five independent boiler variables for TSP emissions estimation. Data from the data acquisition system of a 350 MW industrial boiler were used for model development and validation. The results show that polynomial expansion basic functions are an excellent approach to solve modelling problems related to complex non-linear systems in the industry.

Physicochemical, Interaction & Topological Descriptors vs. hMAO-A Inhibition of Aplysinopsin Analogs: A Boulevard to the Discovery of Semi-Synthetic Anti-Depression Agents

2021 ◽  
Vol 22 ◽  
Author(s):  
Rajeev K. Singla ◽  
Ghulam Md Ashraf ◽  
Magdah Ganash ◽  
Varadaraj Bhat G ◽  
Bairong Shen
Keyword(s):  
Model Development ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Topological Descriptors ◽  
Good Prediction ◽  
Physicochemical Interaction ◽  
Human Beings ◽  
2D Qsar

Background: Neurological disorder, depression is the globally 4th leading cause of chronic disabilities in human beings. Objective: This study aimed to model a 2D-QSAR equation that can facilitate the researchers to design better aplysinopsin analogs with potent hMAO-A inhibition. Methods: Aplysinopsin analogs dataset were subjected to ADME assessment for drug-likeness suitability using StarDrop software before modeled equation. 2D-QSAR equations were generated using VLife MDS 4.6. Dataset was segregated into training and test set using different methodologies, followed by variable selection. Model development was done using principal component regression, partial least square regression, and multiple regression. Results: The dataset has successfully qualified the drug-likeness criteria in ADME simulation, with more than 90% of molecules cleared the ideal conditions including intrinsic solubility, hydrophobicity, CYP3A4 2C9pKi, hERG pIC50, etc. 112 models were developed using multiparametric consideration of methodologies. The best six models were discussed with their extent of significance and prediction capabilities. ALP97 was emerged out as the most significant model out of all, with ~83% of the variance in the training set, the internal predictive ability of ~74% while having the external predictive capability of ~79%. Conclusion: ADME assessment suggested that aplysinopsin analogs are worth investigating. Interaction among the descriptors in a way of summation or multiplication products, are quite influential and yielding significant 2D-QSAR models with good prediction efficiency. This model can be used for the design of a more potent hMAO-A inhibitor having an aplysinopsin scaffold, which can then contribute to the treatment of depression and other neurological disorders.

scholarly journals Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation

2017 ◽  
Author(s):  
Jack Weatheritt ◽  
Richard Pichler ◽  
Richard D. Sandberg ◽  
Gregory Laskowski ◽  
Vittorio Michelassi
Keyword(s):  
High Pressure ◽  
Evolutionary Algorithm ◽  
Turbine Blade ◽  
A Priori ◽  
Model Development ◽  
Boussinesq Approximation ◽  
High Pressure Turbine ◽  
Stress Strain ◽  
Non Linear ◽  
Anisotropy Tensor

The validity of the Boussinesq approximation in the wake behind a high-pressure turbine blade is explored. We probe the mathematical assumptions of such a relationship by employing a least-squares technique. Next, we use an evolutionary algorithm to modify the anisotropy tensor a priori using highly resolved LES data. In the latter case we build a non-linear stress-strain relationship. Results show that the standard eddy-viscosity assumption underpredicts turbulent diffusion and is theoretically invalid. By increasing the coefficient of the linear term, the farwake prediction shows minor improvement. By using additional non-linear terms in the stress-strain coupling relationship, created by the evolutionary algorithm, the near-wake can also be improved upon. Terms created by the algorithm are scrutinized and the discussion is closed by suggesting a tentative non-linear expression for the Reynolds stress, suitable for the wake behind a high-pressure turbine blade.

scholarly journals ANALISIS PERSAMAAN SIMULTAN DARI TOBIN’S Q, KEPEMILIKAN MANAJERIAL DAN KEBIJAKAN UTANG

2007 ◽  
Vol 9 (1) ◽  
pp. 93
Author(s):  
Wahidawati Wahidawati
Keyword(s):  
Firm Value ◽  
Simultaneous Equation ◽  
Least Square ◽  
Managerial Ownership ◽  
Tobin’S Q ◽  
Agency Conflicts ◽  
Manufacturing Companies ◽  
Tobin's Q ◽  
Non Linear ◽  
Linear Simultaneous Equation

The purpose of this research to estimates a simultaneous equations model with Tobin’s Q (firm value), managerial ownership and debt policy jointly determined within the system. This research is based on the pre¬vious¬ research by Chen and Steiner (2000), which found that managerial ownership tobe a significant and positive determinant of the level Tobin’s Q. Chen and Steiner (1999) observed evidence of subtituons-monitoring effects between managerial ownership and debt policy.The research is focused on manufacturing companies listed in BEJ for periode 1999-2002.The method of data collection is done by using pooling method and give 151 firm year observation.This research uses a non linear simultaneous equation methodology with use the statistical method two stage least square. The result of research provides empirical evidence : First, that managerial ownership has a non linear relation with Tobin’s Q (we find support for both an alignmenteffect and an entrenchment effectin the relationship between managerial ownership and Tobin’s Q. second, The result supports the argument that managerial ownership helps to resolve the agency conflicts between stock holder and manager. The result also indicate that there is not subtitu¬ta¬bi¬li¬ty between debt and managerial ownership.

scholarly journals Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Molecules ◽  
2020 ◽  
Vol 25 (12) ◽  
pp. 2919
Author(s):  
Natasa P. Kalogiouri ◽  
Reza Aalizadeh ◽  
Marilena E. Dasenaki ◽  
Nikolaos S. Thomaidis
Keyword(s):  
High Performance ◽  
Model Development ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Table Olives ◽  
Method Performance ◽  
Validation Parameters ◽  
Targeted Screening ◽  
Mass Spectrometric Approach

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

scholarly journals Prediction of macro- and microelements content in Croatian common bean landraces (Phaseolus vulgaris L.) by NIR spectroscopy

Poljoprivreda ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 48-55
Author(s):  
Marina Vranić ◽  
Marko Petek ◽  
Krešimir Bošnjak ◽  
Boris Lazarević ◽  
Klaudija Carović Stanko
Keyword(s):  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Near Infrared ◽  
Model Development ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Potassium Oxide ◽  
Phaseolus Vulgaris L

In this study, near-infrared spectroscopy (NIRS) was used to predict the contents of essential macro- and microelements in common bean (Phaseolus vulgaris L.) accessions of most widespread Croatian landraces. Total of 175 samples were used for the model development by modified partial least square (MPLS), principal component regression (PCR) and partial least square (PLS) techniques. Based on the coefficients of determination (R2), standard error of calibration (SEC) and error of prediction (SEP) the models developed were (i) nearly applicable for nitrogen (N) (0.89, 0.12 and 0.45 respectively), (ii) poor for iron (Fe), cinc (Zn), potassium oxide (K2O) and potassium (K), (iii) usable for phosphorus pentoxide (P2O5), phosphorus (P), phytic acid (PA) and manganese (Mn). The MPLS regression statistics suggested the most accurate models developed comparing with PLS and PCR. It was concluded that a wider set of common bean samples needs to be used for macro- and microelements prediction by NIRS.

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