scholarly journals Dirt Loss Estimator for Photovoltaic Modules Using Model Predictive Control

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1738
Author(s):  
Ricardo R. Santos ◽  
Edson A. Batista ◽  
Moacyr A. G. de Brito ◽  
David D. D. Quinelato
Keyword(s):  
Mathematical Modeling ◽  
Predictive Control ◽  
Predictive Modeling ◽  
Physical Damage ◽  
Photovoltaic Generation ◽  
Unit Process ◽  
Photovoltaic Modules ◽  
Photovoltaic Plants ◽  
Loss Estimator ◽  
Generation Unit

The central problem tackled in this article is the susceptibility of the solar modules to dirt that culminates in losses in energy generation or even physical damage. In this context, a solution is presented to enable the estimates of dirt losses in photovoltaic generation units. The proposed solution is based on the mathematical modeling of the solar cells and predictive modeling concepts. A device was designed and developed to acquire data from the photovoltaic unit; process them based on a predictive model, and send loss estimates in the generation unit to a web server to help in decision-making support. The results demonstrated the real applicability of the system to estimate losses due to dirt or electrical mismatches in photovoltaic plants.

Predictive Modeling as guide for Health Informatics Deployment

2017 ◽  
pp. 128-162
Author(s):  
Fabrizio L. Ricci ◽  
Oscar Tamburis
Keyword(s):  
Mathematical Modeling ◽  
Predictive Modeling ◽  
Health Informatics ◽  
Research Work ◽  
Clinical Information ◽  
Healthcare Sector ◽  
Adoption Process ◽  
Design Features ◽  
Care Processes ◽  
Ex Ante

The present research work shows the main steps conducted towards the exploitation of the LUMIR project, aiming at realizing an EHR framework in the Italian Region of Basilicata (also known as Lucania). It relates to a structure of network–enabled services capable of integrating the ICT solutions used by the operators of the Healthcare System of Basilicata Region. The adoption process of the LuMiR system was meant to address the issues connected to the design features as well as to the EHR diffusion and the acceptance aspects. The mathematical modeling approach introduced aimed at making possible to get to a measure “ex–ante” of both adequacy and significance of the adoption process itself. The final intent is to work out a scalable and exportable model of advanced management of clinical information, towards a stronger cooperation among the provider organizations and a better governance of care processes, as crucial element within the more general path of modernization of the healthcare sector.

Neural Wiener Based Model Predictive Control (NWMPC) for MTBE Catalytic Distillation Using Reduced Sequential Quadratic Programming (RSQP)

2015 ◽  
Vol 1113 ◽  
pp. 733-738
Author(s):  
Sudibyo Sudibyo ◽  
Muhamad Nazri Murat ◽  
Norashid Aziz
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Reactive Distillation ◽  
Nonlinear Behavior ◽  
Strong Interactions ◽  
Catalytic Distillation ◽  
Unit Process ◽  
Reaction Conversion ◽  
Highly Nonlinear ◽  
Control Study

Reactive distillation is a process that combines both reactor and distillation column in one unit process. The reactive distillation is normally applied in MTBE production in order to achieve high reaction conversion and purity of the MTBE. Controlling such reactive distillation is a challenging task due to its highly nonlinear behavior and the existence of strong interactions among control variables. In this work, a Neural Wiener based model predictive control (NWMPC) is designed and implemented to control the tray temperature of MTBE reactive distillation. The Reduced SQP (RSQP) has been embedded as an optimizer in the NWMPC proposed. The MTBE reactive distillation has been modeled using aspen dynamic and the control study has been simulated using Simulink (Matlab) which is integrated with Aspen dynamic model. The results achieved show that the NWMPC is able to maintain tray temperatures at desired set points, able to reject the disturbance and robust toward robustness test conducted.

scholarly journals FORCES PERFORMANCE OF TRAWL SYSTEM – II: PHYSICAL MODELING

2021 ◽  
Vol 2021 (3) ◽  
pp. 86-93
Author(s):  
Alexander Alekseevich Nedostup ◽  
Alexey Olegovich Razhev
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Predictive Modeling ◽  
Similarity Theory ◽  
Time Derivative ◽  
Complex Nature ◽  
Combining Data ◽  
Conducting Research ◽  
Self Learning ◽  
Physical Quantities

The article is a continuation of scientific research and justification of the possibility of artificial intelligence technologies for the tasks of predictive modeling of the behavior of a trawl system in the process of fishing on a self-learning neural network. The definition of the productivity of forces is introduced - the second time derivative of the work of these forces. The intermediate result of the design of the trawl system is a project - an integrated set of characteristics described in a form suitable for its operation with a given performance of forces. To proceed to predictive modeling, it is necessary to determine the extent of similarity of the trawl system in different areas of its interaction. There is inter-discipline, which is manifested in the formulation of problems, in ap-proaches to their solution, in revealing the connections between theories, in the formation of new disciplines. Interdisciplinarity allows conducting research with the trawl system in its entirety, combining data from various disciplines (hydromechanics, electrodynamics, thermodynamics, acoustics, optics, etc.), leading to the emergence of new postulates and laws that synthesize the sci-entific knowledge necessary for a self-learning neural network of fishing for the trawl system. To combine the knowledge there was chosen the similarity theory as a mathematical modeling method based on the transition from ordinary physical quantities that affect the system being modeled to generalized complex-type quantities composed of original physical quantities, but in certain combinations, depending - from the specific nature of the process under study. The complex nature of these quantities has a deep physical meaning of reflecting the interaction of various influences. The similarity theory studies the methods of constructing and applying these variables and is used in cases of mathematical modeling when an analytical solution of mathematical modeling problems is impossible due to complexity and accuracy requirements. The similarity theory is used in these cases to synthesize relations obtained on the basis of the physical mechanism of the process under study and data of a numerical solution or experiment.

Passive Thermal Management of Photovoltaic Modules—Mathematical Modeling and Simulation of Photovoltaic Modules

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Abdelhakim Hassabou ◽  
Ahmed Abotaleb ◽  
Amir Abdallah
Keyword(s):  
Mathematical Modeling ◽  
Thermal Management ◽  
Phase Change Materials ◽  
Thick Layer ◽  
Vapor Condensation ◽  
Solar Pv ◽  
Photovoltaic Modules ◽  
Pv Systems ◽  
Pv Modules ◽  
Testing Condition

Operation of solar photovoltaic (PV) systems under high temperatures and high humidity represents one of the major challenges to guarantee higher system’s performance and reliability. The PV conversion efficiency degrades considerably at higher temperatures, while dust accumulation on PV module together with atmospheric water vapor condensation may cause a thick layer of mud that is difficult to be removed. Therefore, thermal management in hot climates is crucial for reliable application of PV systems to prevent the efficiency to drop due to temperature rise. This research focuses on the utilization of phase-change materials (PCM) for passive thermal management of solar systems. The main focus is to explore the effect of utilization of PCM-based cooling elements on the thermal behavior of solar PV modules. This paper presents the mathematical modeling and validation of PV modules. Both simulation and experimental data showed that the significant increase in PV peak temperature in summer affects the module’s efficiency, and consequently produced power, by 3% compared to standard testing condition (STC) as an average over the entire day, while it goes up to 8% and 10% during peak noon hours in winter and summer, respectively.

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