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

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

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

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%

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

An enhanced scatter search with combined opposition-based learning for parameter estimation in large-scale kinetic models of biochemical systems

An enhanced scatter search (eSS) with combined opposition-based learning algorithm is proposed to solve large-scale parameter estimation in kinetic models of biochemical systems. The proposed algorithm is an extension of eSS with three important improvements in terms of: reference set (RefSet) formation, RefSet combination, and RefSet intensification. Due to the difficulty in estimating kinetic parameter values in the presence of noise and large number of parameters (high-dimension), the aforementioned eSS mechanisms have been improved using combination of quasi-opposition and quasi-reflection, which were under the family of opposition-based learning scheme. The proposed algorithm is tested using one set of benchmark function each from large-scale global optimization (LSGO) problem as well as parameter estimation problem. The LSGO problem consists of 11 functions with 1000 dimensions. For parameter estimation, around 116 kinetic parameters in Chinese hamster ovary (CHO) cells and central carbon metabolism of E. coli are estimated. The results revealed that the proposed algorithm is superior to eSS and other competitive algorithms in terms of its efficiency in minimizing objective function value and having faster convergence rate. The proposed algorithm also required lower computational resources, especially number of function evaluations performed and computation time. In addition, the estimated kinetic parameter values obtained from the proposed algorithm produced the best fit to a set of experimental data

Fault identification in pipeline system using normalized hilbert huang transform and automatic selection of intrinsic mode function

Pressure transient analysis have been widely studied for monitoring of pipeline healthy and condition assessment in water distribution systems. This technique has potential low cost, non intrusive nature and able to locate any uncertainties (leak, pipe fitting, blockage) at greater distance from measurement point. In this research the application of Normalised Hilbert Huang Transform (NHHT) as the method to analyse the pressure transient signal. However, this method has the difficulty in selecting the suitable IMF for the further data analysing method which is Normalised Hilbert Transform (NHT). This paper proposed to apply Integrated Kurtosis-based Algorithm for z-filter Technique (Ikaz) for that allows automatic selection of intrinsic mode function (IMF) that’s should be used. This work demonstrates on 67.9-meter Medium High Density Poly Ethylene (MDPE) pipe installed with single artificial leak simulator with water pressure about 1-4 bar. The analysis results using Normalized Ikaz proven that the method can be apply as an automatic selection of intrinsic mode function (IMF) although the noise level ratio of the signal is lower. Normalized Ikaz is recommended and advised to be implemented as automatic selection of intrinsic mode function (IMF) through NHHT analysis

A Study of Ikaz and Normalized Hilbert transform for solving faulty in pipeline distribution system using transmission line modelling

When there are sudden changes in fluid propagation in the pipeline system, pressure transient signal is generated. Due to the rapid pressure and fluctuation flow of the system such as opening and closing of valve rapidly. A few group of researchers had use the pressure transient signal to detect and locate any uncertainties in the system (leak and blockage). Empirical Mode decomposition (EMD) will be as the demonizing method of pressure transient signal before proceeding to be analyzed further by using instantaneous frequency analysis in this research. EMD might be the step of decomposing the signal into intrinsic mode function, but this method have difficulties in selecting a suitable IMF. This paper proposed the uses of Integrated Kurtosis-based Algorithm for z-filter Technique (Ikaz) for that allows automatic selection of suitable and relevant IMF. This work shows the artificial pressure transient signal generates using transmission line modelling (TLM) in order to test the effectiveness of Ikaz as the autonomous selection of IMF. This paper implements the Normalize Hilbert Transform (NHT) as the instantaneous frequency analysis. A straight fluid network was designed using TLM fixing with higher resistance at some point that act as a leak and connecting to the pipe feature such as junction, pipefitting or blockage. The analysis results using Ikaz show that the method can be implement as an automatic selection of intrinsic mode function (IMF) with percentage errors below 5%. Thus, Ikaz-kurtosis ratio is recommended to be implemented as automatic selection of intrinsic mode function (IMF) through NHT analysis