Optimization of salt crystallization process by solar energy with the use of mirror reflection, case of Chott Merouane El-Oued (South East of Algeria)

Purpose. This paper aims to improve the harvesting conditions of the crystallized salt layer of the Salins Merouane El Meghaier (SME) – South East of Algeria, by creating favorable conditions for means of harvesting (harvesters), thanks to the acceleration of evaporation-crystallization process of salt by using an installation of flat mirrors, which reflect solar radiation towards the evaporating surface. Methods. To achieve the objectives, a stall installation contains pans equipped with different mirror surfaces. Compared with other designs, this test unit is installed near the Chott during the months of December and January. Findings. The optimization rate of salt evaporation-crystallization process depends on the surface of the reflection mirror used, which allows obtaining a layer of soft salt easy to harvest during the winter months. Originality. The use of mirrors reflecting solar radiation in salt pans of the unit in Salins Merouane El Meghaier enables to improve the salt exploitation conditions in quantitative, qualitative and economic terms, and to minimize the occupation of agriculture area. Practical implications. The exploitation of solar energy for salt production at the unit in Salins Merouane El Meghaier represents a free source, which is inexhaustible and produces no harmful impact on the environment.Мета. Оптимізація умов збору шару кристалізованої солі на солончаках озер Меруан і Мельгир у південно-східному Алжирі на основі прискорення процесів її випаровування й кристалізації із використанням системи плоских дзеркал, відбиваючих сонячну радіацію на поверхню, що випаровується. Методика. Для досягнення поставленої мети було виконано моніторинг змін кліматичних параметрів з 1975 по 2010 роки. Розроблено дослідну установку поблизу озера Меруан, що складається з чанів, заповнених розсолом товщиною 120 мм кожен, розміщених у землі та оснащених плоскими простими дзеркалами і дзеркалами, що захоплюють сонячні промені й відбивають їх до поверхні розсолу. Випробування проводилися в період з 12.12.2016 по 1.02.2017. З 09:00 ранку до 16:00 вечора дзеркала рухались за сонцем за допомогою регулювання кутів до положення сонця (азимут і висота). Щодня реєструвалася швидкість вітру, вологість і особливо випаровування, що було зроблено з використанням лінійки, закріпленої у стінці кожного чану. Результати. Встановлено, що для утворення кристалізованого сольового шару завтовшки 40 мм, що підходить для збору, в чанах P1 і P2, оснащених плоскими простими дзеркалами (SM), потрібно 52 дні, а в чанах з дзеркалами P0, що захоплюють сонячні промені й відбивають їх до поверхні розсолу (GM1) – 41 і 43 дня відповідно. Приріст у 9 днів отриманий завдяки використанню SM і 11 днів – GM1, а швидкість оптимізації процесу кристалізації склала 17%, якщо поверхня дзеркала становить 31.49% поверхні розсолу в чані (P2) і 21%, якщо поверхня дзеркала становить 77% поверхні розсолу в чані (P2). Визначено, що майже всі хімічні аналізи солі в чанах ідентичні, вміст галіту становить 95.80 – 95.97%, тобто сонячна радіація не впливає на якість солі. Наукова новизна. Доведено, що процес випаровування та кристалізації солі залежать від розміру поверхні відбиваючого дзеркала, що дозволяє отримати шар м’якої солі, легковидобувної у зимовий період. Практична значимість. Використання дзеркал, що відбивають сонячну радіацію в солезбірних чанах установки на солончаках озер Меруан і Мельгір, покращує кількісні, якісні та економічні показники, а також дозволяє звести до мінімуму задіяні сільськогосподарські території.Цель. Оптимизация условий сбора слоя кристаллизованной соли на солончаках озер Меруан и Мельгир в юго-восточном Алжире на основе ускорения процессов ее испарения и кристаллизации с использованием системы плоских зеркал, отражающих солнечную радиацию на испаряющуюся поверхность. Методика. Для достижения поставленной цели был выполнен мониторинг за изменениями климатических параметров с 1975 по 2010 годы. Смонтирована опытная установка вблизи Chott Merouane, состоящая из чанов, заполненных рассолом толщиной 120 мм каждый, размещенных в земле, оснащенных плоскими простыми зеркалами и зеркалами, захватывающими солнечные лучи и отражающими их к поверхности рассола. Испытание проводилось в период с 12.12.2016 по 1.02.2017. С 09:00 утра до 16:00 вечера зеркала следовали за движением солнца с помощью регулировки углов к положению солнца (азимут и высота). Ежедневно регистрировалась скорость ветра, влажность и особенно испарение, что было сделано с использованием линейки, закрепленной в стенке каждого чана. Результаты. Установлено, что для образования кристаллизованного солевого слоя толщиной 40 мм, подходящего для сбора, в чанах P1 и P2, оснащенных плоскими простыми зеркалами (SM), потребовалось 52 дня, а в чанах с зеркалами P0, захватывающими солнечные лучи и отражающими их к поверхности рассола (GM1) – 41 и 43 дня соответственно. Прирост в 9 дней получен благодаря использованию SM и 11 дней – GM1, а скорость оптимизации процесса кристаллизации составила 17%, если поверхность зеркала представляет собой 31.49% поверхности рассола в чане (P2) и 21%, если поверхность зеркала составляет 77% поверхности рассола в чане (P2). Определено, что почти все химические анализы соли в чанах идентичны, содержание галита составляет 95.80 – 95.97%, то есть, что что солнечная радиация не влияет на качество соли. Научная новизна. Доказано, что процесс испарения и кристаллизации соли зависит от размера поверхности отражающего зеркала, что позволяет получить слой мягкой соли, легкодобываемый в зимний период. Практическая значимость. Использование зеркал, отражающих солнечную радиацию в солесборных чанах установки на солончаках озер Меруан и Мельгир, улучшает количественные, качественные и экономические показатели, а также позволяет свести к минимуму задействованные сельскохозяйственные территории.The authors are gratefully acknowledging the Badji Mokhtar University and Larbi Tebessi for provided the assistance to carry out this scientific study

Leakage identification based on hydraulic transient analysis

Due to the ever-increasing degree of water loss, researchers and water utility corporations are becoming increasingly concerned about water leakage control. The purpose of this paper is to apply Empirical Mode Decomposition (EMD) and Synchrosqueezed Wavelet Transforms (SWT) as signal processing to locate leaks in pipelines. The objective of this study is related with to investigate leakage detection and signal processing methods, as well as to use them to detect and locate leaks. This paper explains how to run an experiment to visualize the most common types of leakage in a pipeline system. The experiment was set up that include a specific component of the piping system and leakage attached to it. This experimental test rig also attached with pressure sensor at the top of the solenoid valve. The piezoelectric pressure sensor is used in this experiment. The findings show that the method is superior to current signal processing methods for the conditions used. The recommendation is that research can be extended by running field test in order to observe the efficiency of the method used

Implementation of I-kaz with Teager-Kaiser energy operator in solving leakage problem using synthetic signal

Transient event usually happen due to pressure surge inside water pipeline network by either opening or closing valve rapidly or water hammer phenomena. This paper focus on identification of leak signature using Empirical Mode Decomposition (EMD) with the implementation of Ikaz-kurtosis ratio while Teager Kaiser Energy Operator (TKEO) use as instantaneous frequency analysis (IFA). Two synthetic signal with different pipe characteristics was construct using transmission line modelling (TLM). It is show that Ikaz-kurtosis ratio give good result in selecting the intrinsic mode function (IMF) after EMD decomposed the signal into a series of IMFs. TKEO as post processing analysis extract all the information inside the signal that contaminated with noise. Its show that leakage position can be localize with maximum error less 7.3%. Meanwhile, Outlet position recorded 3.4% maximum. This conclude that this method apply for synthetic signal is acceptable for leakage detection

Enhanced directed random walk for the identification of breast cancer prognostic markers from multiclass expression data

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal‐like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expres-sions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer sub-types and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one‐way ANOVA (F‐test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. There-fore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes

Pipeline fault identification using synchrosqueezed wavelet transform based on pressure transient analysis

Brand modern technology of leak detection by using pressure transient analysis has been developed and interested to research due to its advantages such as low cost, simplicity and convenient to use. This technology uses the concept of signal reflections which identify pipeline features. The method used in this study was using a pressure transducer (piezoelectric pressure sensor) to obtain pressure transient respond generated by rapid opening and closing of solenoid valve. However, such reflections are very difficult to determine the pipe characteristic most probably because of excessive noise from other sources. Therefore, this paper proposed a method called Empirical Mode Decomposition (EMD) to decompose the reflection signal to its Intrinsic Mode Function (IMFs) and further analysis using continuous wavelet transform (CWT) to transform the signal into Time-Frequency domain and spectrum diagram. From the spectrum diagram, the characteristic of the pipe can be clearly display. From the finding results, it proves that this method not only useful for leak detection but also can determine the location of leak and its magnitude with error less than 10%

A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data

Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications

Pipe Leak Diagnostic Using High Frequency Piezoelectric Pressure Sensor And Automatic Selection Of Intrinsic Mode Function

In a recent study, the analysis of pressure transient signals could be seen as an accurate and low-cost method for leak and feature detection in water distribution systems. Transient phenomena occurs due to sudden changes in the fluid's propagation in pipelines system caused by rapid pressure and flow fluctuation due to events such as closing and opening valves rapidly or through pump failure. In this paper, the feasibility of the Hilbert-Huang transform (HHT) method/technique in analysing the pressure transient signals in presented and discussed. HHT is a way to decompose a signal into intrinsic mode functions (IMF). However, the advantage of HHT is its difficulty in selecting the suitable IMF for the next data postprocessing method which is Hilbert Transform (HT). This paper reveals that utilizing the application of an integrated kurtosis-based algorithm for a z-filter technique (I-Kaz) to kurtosis ratio (I-Kaz-Kurtosis) allows/contributes to/leads to automatic selection of the IMF that should be used. This technique is demonstrated on a 57.90-meter medium high-density polyethylene (MDPE) pipe installed with a single artificial leak. The analysis results using the I-Kaz-kurtosis ratio revealed/confirmed that the method can be used as an automatic selection of the IMF although the noise level ratio of the signal is low. Therefore, the I-Kaz-kurtosis ratio method is recommended as a means to implement an automatic selection technique of the IMF for HHT analysis

The Use of Transmission Line Modelling to Test the Effectiveness of I-kaz as Autonomous Selection of Intrinsic Mode Function

Pressure transient signal occurred due to sudden changes in fluid propagation filled in pipelines system, which is caused by rapid pressure and flow fluctuation in a system, such as closing and opening valve rapidly. The application of Hilbert-Huang Transform (HHT) as the method to analyse the pressure transient signal utilised in this research. However, this method has the difficulty in selecting the suitable IMF for the further post-processing, which is Hilbert Transform (HT). This paper proposed the implementation of Integrated Kurtosis-based Algorithm for z-filter Technique (I-kaz) to kurtosis ratio (I-kaz-Kurtosis) for that allows automatic selection of intrinsic mode function (IMF) that's should be used. This work demonstrated the synthetic pressure transient signal generates using transmission line modelling (TLM) in order to test the effectiveness of I-kaz as the autonomous selection of intrinsic mode function (IMF). A straight fluid network was designed using TLM fixing with higher resistance at some point act as a leak and connecting to the pipe feature (junction, pipefitting or blockage). The analysis results using I-kaz-kurtosis ratio revealed that the method can be utilised as an automatic selection of intrinsic mode function (IMF) although the noise level ratio of the signal is lower. I-kaz-kurtosis ratio is recommended and advised to be implemented as automatic selection of intrinsic mode function (IMF) through HHT analysis

Leak localization using empirical mode decomposition and teager-kaiser energy operator analysis based on pressure transient signal: experimental study

Leak detection become crucial part in water management services due to strenuous work in identification of leak location for pipeline networks. This paper focused on leak identification and localization using Teiger-Kaiser Energy Operator (TKEO) as instantaneous frequency analysis (IFA) while Empirical Mode Decomposition (EMD) as decomposition method with implementation of Integrated Kurtosis Algorithm for Z-Filter (Ikaz) to Kurtosis ratio as the automatic selection criterion for intrinsic mode function (IMF). Test rig construct inside laboratory as testing site using 67.90-metre Medium Density Polyethylene (MDPE) pipe In order to create an artificial leak, pinhole is drill at 19.75-metre distance from point of analysis that is fire hydrant attached with pressure sensor. Experiment conduct by using two variation of pressure that are 2bar and 4bar. As the result, with percentage of error less than 6%, combination of TKEO as IFA and efficiency of Ikaz performed well in locating the position of leak and outlet of pipeline system

Fault Identification in Pipeline System Using Normalized Hilbert Huang Transform and Automatic Selection of Intrinsic Mode Function

Pressure transient analysis has been widely used to monitor the condition of pipelines and its assessment in water distribution systems. This is a low-cost nonintrusive technique with the ability to locate uncertainties (leak, pipe fitting, blockage) at a greater distance from the measurement point. In this research, Normalised Hilbert Huang Transform (NHHT) is used as the method to analyse the pressure transient signal. However, this method has difficulty in selecting the suitable intrinsic mode function (IMF) for the advance data analysing. As an alternative, Integrated Kurtosis-based Algorithm for z-filter Technique (Ikaz), which allows automatic selection of intrinsic mode function (IMF) been used to substitute the NHHT limitation in this study. The analysis is conducted on a 67.9-meter Medium High-Density PolyEthylene (MDPE) pipe installed with single artificial leak simulator at a water pressure in the range of 1-4 bar