Very Fast and Accurate Procedure for the Characterization of Photovoltaic Panels from Datasheet Information

In recent years several numerical methods have been proposed to identify the five-parameter model of photovoltaic panels from manufacturer datasheets also by introducing simplification or approximation techniques. In this paper we present a fast and accurate procedure for obtaining the parameters of the five-parameter model by starting from its reduced form. The procedure allows characterizing, in few seconds, thousands of photovoltaic panels present on the standard databases. It introduces and takes advantage of further important mathematical considerations without any model simplifications or data approximations. In particular the five parameters are divided in two groups, independent and dependent parameters, in order to reduce the dimensions of the search space. The partitioning of the parameters provides a strong advantage in terms of convergence, computational costs, and execution time of the present approach. Validations on thousands of photovoltaic panels are presented that show how it is possible to make easy and efficient the extraction process of the five parameters, without taking care of choosing a specific solver algorithm but simply by using any deterministic optimization/minimization technique.

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Optimization and characterization of flavonoids extracted from Cannabis sativa fibers

Textile Research Journal ◽

10.1177/00405175211027796 ◽

2021 ◽

pp. 004051752110277

Author(s):

Qilu Cui ◽

Jiawei Li ◽

Chongwen Yu

Keyword(s):

Chemical Structure ◽

Cannabis Sativa ◽

Ethanol Concentration ◽

Extraction Process ◽

Test Results ◽

Hemp Fiber ◽

Hemp Fibers ◽

Structure Surface ◽

Degumming Process

In this paper, the extraction process of flavonoids from hemp fibers was studied. Response surface methodology (RSM) analysis of the extraction parameters indicated that optimized results would be ethanol concentration 76 vol.%, bath ratio 1:50, and reaction time 139 min; therefore, an optimal extraction rate of flavonoids of 0.2275% can be obtained. The chemical structure, surface morphology and element composition of flavonoid extracts were analyzed. The test results indicated that hemp extract contains flavonoids, which can be used to extract flavonoids from hemp fiber, so as to comprehensively develop hemp fiber and reduce the discharge of waste liquid in the traditional degumming process.

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Enzymatic Extraction and Characterization of Pectin from Cocoa Pod Husks (Theobroma cacao L.) Using Celluclast® 1.5 L

Molecules ◽

10.3390/molecules26051473 ◽

2021 ◽

Vol 26 (5) ◽

pp. 1473

Author(s):

Licelander Hennessey-Ramos ◽

Walter Murillo-Arango ◽

Juliana Vasco-Correa ◽

Isabel Cristina Paz Astudillo

Keyword(s):

Acid Content ◽

Galacturonic Acid ◽

Extraction Process ◽

Chemical Extraction ◽

Enzymatic Extraction ◽

Acid Yield ◽

Physicochemical Features ◽

Enzyme Dosage ◽

Cocoa Pod Husks

Cocoa pod husks are a waste generated during the processing of cocoa beans. We aimed to explore the enzymatic extraction of pectin using cellulases. The extraction process was optimized using a central composite design (CCD) and analyzed by response surface methodology (RSM). The parameters optimized were feedstock concentration (%), enzyme dosage (µL/g), and time (h). Three dependent variables were studied: pectin yield (g/100 g dry husk) (R2 = 97.02), galacturonic acid content (g/100 g pectin) (R2 = 96.90), and galacturonic acid yield (g/100 g feedstock) (R2 = 95.35). The optimal parameters were 6.0% feedstock concentration, 40 µL g−1 of enzyme, and 18.54 h, conditions that produced experimentally a pectin yield of 10.20 g/100 g feedstock, 52.06 g galacturonic acid/100 g pectin, and a yield 5.31 g galacturonic acid/100 g feedstock. Using the chemical extraction method, a yield of 8.08 g pectin/100 g feedstock and a galacturonic acid content of 60.97 g/100 g pectin were obtained. Using assisted sonication, a pectin yield of 8.28 g/100 g feedstock and a galacturonic acid content of 42.77 g/100 g pectin were obtained. Enzymatically optimized pectin has rheological and physicochemical features typical of this biomaterial, which provides an interesting alternative for the valorization of cocoa husks.

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Preliminary Study of Alginates Extracted from Brown Algae (Sargassum sp.) Available in Madura Island as Composite Based Hydrogel Materials

Materials Science Forum ◽

10.4028/www.scientific.net/msf.964.240 ◽

2019 ◽

Vol 964 ◽

pp. 240-245 ◽

Cited By ~ 1

Author(s):

Amaliya Rasyida ◽

Thalyta Rizkha Pradipta ◽

Sigit Tri Wicaksono ◽

Vania Mitha Pratiwi ◽

Yeny Widya Rakhmawati

Keyword(s):

Sodium Alginate ◽

Brown Algae ◽

Extraction Process ◽

Composition Analysis ◽

3D Printer ◽

X Ray Diffraction ◽

X Ray ◽

3D Printed ◽

Preliminary Study

Utilization of brown algae especially in Madura, where it’s close to Surabaya, only limited for food. This become a reference for developing and increasing the potential of this algae by extracting one of the ingredients, namely alginate. This paper deals with the characterization of sodium alginate extracted from sargassum sp. using modified-purified calcium routes. The extracted sodium alginate will be further used as composite hydrogel materials and compared with commercial sodium alginate. Hereafter, the synthesized composite is expected to be bio-ink for 3d printer. Chemical composition analysis were analyzed using X-Ray Fluorosense (XRF) followed by Fourier-transform infrared spectroscopy (FTIR) analysis to identify the functional group of composite and X-Ray Diffraction (XRD). Furthermore, viscosity bath is performed to compare the viscosity of extracted and commercial one. The result shows that modified-purified calcium routes in the extraction process of sodium alginate is desirable for improving their properties. Interestingly enough, with the goal of using it as bio-ink in 3d printed fabrication, the synthesized composite shows viscosity, 300 cSt, which meets the criteria for bio-ink in 3d printer.

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LIBS for the Analysis of Metallurgical and Solar Grade Silicon

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.324.324 ◽

2011 ◽

Vol 324 ◽

pp. 324-327 ◽

Cited By ~ 1

Author(s):

Sarah Darwiche ◽

Malek Benmansour ◽

Nir Eliezer ◽

Daniel Morvan

Keyword(s):

Chemical Characterization ◽

Laser Induced Breakdown Spectroscopy ◽

Solar Grade Silicon ◽

Photovoltaic Panels ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown ◽

Elemental Chemical Analysis ◽

Boron Analysis ◽

Grade Silicon

Laser-induced breakdown spectroscopy (LIBS) has been employed for the fast and reliable chemical characterization of silicon used for the photovoltaic industry. Silicon for photovoltaic panels is subject to certain constraints on its purity, and notably must contain low concentration of boron. The use of LIBS could be advantageous because it allows rapid and simultaneous multi-elemental chemical analysis of silicon without any sample preparation. LIBS was applied to boron analysis and a detection limit of 0.23 ppmw was found for optimized gas and pressure conditions.

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A characterization of optimal multiprocessor schedules and new dominance rules

Journal of Combinatorial Optimization ◽

10.1007/s10878-020-00634-9 ◽

2020 ◽

Vol 40 (4) ◽

pp. 876-900

Author(s):

Rico Walter ◽

Alexander Lawrinenko

Keyword(s):

Parallel Machines ◽

Computational Study ◽

Search Space ◽

Point Of View ◽

Theoretical Point ◽

Optimal Solutions ◽

Identical Parallel Machines ◽

Makespan Minimization ◽

Structural Pattern

Abstract The paper on hand approaches the classical makespan minimization problem on identical parallel machines from a rather theoretical point of view. Using an approach similar to the idea behind inverse optimization, we identify a general structural pattern of optimal multiprocessor schedules. We also show how to derive new dominance rules from the characteristics of optimal solutions. Results of our computational study attest to the efficacy of the new rules. They are particularly useful in limiting the search space when each machine processes only a few jobs on average.

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Image Retrieval Using Data Mining Technique

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.8.26 ◽

2020 ◽

pp. 2115-2125

Author(s):

Sarmad T. Abdul-Samad ◽

Sawsan Kamal

Keyword(s):

Image Retrieval ◽

User Satisfaction ◽

Clustering Algorithm ◽

Retrieval System ◽

Spatial Information ◽

Search Space ◽

Extraction Process ◽

Retrieval Process ◽

Data Mining Technique ◽

Image Retrieval System

Even though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show that using the spatial features increases the system accuracy and that the clustering algorithm speeds up the image retrieval process. This shows that the proposed system works with texture and non-texture images.

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A New In-line Laser-based Acoustic Technique for Pillar Bump Metrology

International Symposium on Microelectronics ◽

10.4071/isom-2015-wp54 ◽

2015 ◽

Vol 2015 (1) ◽

pp. 000486-000492

Author(s):

Todd W. Murray ◽

Andrew Bakir ◽

David M. Stobbe ◽

Michael J. Kotelyanskii ◽

Robin A. Mair ◽

...

Keyword(s):

Focused Ion Beam ◽

Lower Cost ◽

Ion Beam ◽

High Sensitivity ◽

Reduced Form ◽

Vibrational Modes ◽

Acoustic Technique ◽

Key Drivers ◽

Improved Performance

The drive to reduce the interconnect pitch and increase the number of connections for packaging in mobile devices has led to the development of copper pillar bumps. The key drivers for the adoption of copper pillars are improved performance, reduced form factor and lower cost. In this paper, we present a laser-based acoustic technique for the characterization of multi-layer pillars. This non-contact technique has a high sensitivity for materials characterization with micron-scale spatial resolution. Absorption of laser light causes excitation of elastic waves that propagate through the pillar and are reflected by the pillar walls, exciting vibrational modes in the structure. We have demonstrated that our approach is sensitive to the thicknesses of individual layers in bi-layer and tri-layer copper pillar stacks. Focused ion beam scanning electron microscopy (FIB SEM) has been used to optimize the model and to validate the accuracy of the technique.

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Evolving Deep DenseBlock Architecture Ensembles for Image Classification

Electronics ◽

10.3390/electronics9111880 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1880

Author(s):

Ben Fielding ◽

Li Zhang

Keyword(s):

Search Space ◽

Classification Performance ◽

Learning Mechanisms ◽

Structural Variations ◽

Computational Costs ◽

Search Spaces ◽

Deep Architecture ◽

Architecture Generation ◽

Complex Search Spaces ◽

And Training

Automatic deep architecture generation is a challenging task, owing to the large number of controlling parameters inherent in the construction of deep networks. The combination of these parameters leads to the creation of large, complex search spaces that are feasibly impossible to properly navigate without a huge amount of resources for parallelisation. To deal with such challenges, in this research we propose a Swarm Optimised DenseBlock Architecture Ensemble (SODBAE) method, a joint optimisation and training process that explores a constrained search space over a skeleton DenseBlock Convolutional Neural Network (CNN) architecture. Specifically, we employ novel weight inheritance learning mechanisms, a DenseBlock skeleton architecture, as well as adaptive Particle Swarm Optimisation (PSO) with cosine search coefficients to devise networks whilst maintaining practical computational costs. Moreover, the architecture design takes advantage of recent advancements of the concepts of residual connections and dense connectivity, in order to yield CNN models with a much wider variety of structural variations. The proposed weight inheritance learning schemes perform joint optimisation and training of the architectures to reduce the computational costs. Being evaluated using the CIFAR-10 dataset, the proposed model shows great superiority in classification performance over other state-of-the-art methods while illustrating a greater versatility in architecture generation.

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