A Study of Dispersed, Thermally Activated Limestone from Ukraine for the Safe Liming of Water Using ANN Models

Liming surface water is a fairly popular method of increasing the pH values and decreasing the concentration of phosphates and heavy metals. According to the Environmental Protection Agency (EPA) recommendations, the increase of water pH should not exceed 1.5. If surface water is the source of water supply, liming is a process that reduces water contamination. This should prevent the creation of an additional load for the water treatment plants in urban settlements. This article is an interdisciplinary research study aiming to (1) determine and compare the doses of new dispersed, thermally activated limestone and natural limestone, (2) find the relation between dose value and initial water parameters (pH, Eh and total mineralization), and (3) create an artificial neural network (ANN) model to predict changes in water pH values according to EPA recommendations. Recommended doses were obtained from experimental studies, and those of dispersed, thermally activated limestone were lower than the doses of natural limestone. Neural networks were used to predict the changes in water pH values when adding different doses of limestone with different initial water parameters using the ANN model. Four ANN models with different activation functions and loss function optimizers were tested. The best results were obtained for the network with the ReLU activation function for hidden layers of neurons and Adam’s loss function optimizer (MAPE = 14.1%; R2 = 0.847). Further comparison of the results of the loss function and the results of calculating the quality metric for the training and validation dataset has shown that the created ANN can be used to solve the set research issue.

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Geochemical Variability of Surface Sediment in Post-Mining Lakes Located in the Muskau Arch (Poland) and Its Relation to Water Chemistry

Water Air & Soil Pollution ◽

10.1007/s11270-021-05057-8 ◽

2021 ◽

Vol 232 (3) ◽

Author(s):

Michał Gąsiorowski ◽

Jacek Stienss ◽

Elwira Sienkiewicz ◽

Ilona Sekudewicz

Keyword(s):

Underground Mining ◽

Chemical Compositions ◽

Monte Carlo Test ◽

Water Parameters ◽

Ph Values ◽

Water And Sediment ◽

Mining Lakes ◽

Water Ph ◽

Physical And Chemical ◽

Chemical Water

AbstractWater and sediment chemical compositions were investigated in 75 post-mining lakes in the eastern part of the Muskau Arch region (Western Poland, Central Europe). The lakes, originating from lignite and clay surfaces and underground mining activity, exhibited a wide gradient of physical and chemical water parameters. The most pronounced variability in water parameters was observed in pH values, conductivity, and oxygen concentration in the mixolimnion and monimolimnion. In addition, the lakes varied in terms of their trophic status, indicated by their dissolved organic carbon (DOC) and phosphorous concentrations. In general, lakes with higher pH values had higher trophic states, and some even exhibited hypertrophic conditions. The Monte Carlo test indicated that the water pH, phosphorus, DOC, and silicon concentrations could significantly explain the chemical characteristics of sediments.

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Boron Fertilizer Product and Concentration Influence Spray Water pH

HortTechnology ◽

10.21273/horttech.10.2.350 ◽

2000 ◽

Vol 10 (2) ◽

pp. 350-353

Author(s):

Frank J. Peryea ◽

Jennifer M. Lageschulte

Keyword(s):

Boric Acid ◽

Natural Waters ◽

Agricultural Chemicals ◽

Solution Ph ◽

Initial Water ◽

Multiple Products ◽

Ph Values ◽

Water Composition ◽

Water Ph ◽

The Stability

Farmers often mix fertilizers, pesticides and other agricultural chemicals together in a spray tank to allow applications of multiple products in a single spray. Because polyborate-based B fertilizers may increase solution pH, adding B to tank-mixed sprays may impair the stability and efficacy of alkalinity-sensitive pesticides and growth regulators if an acidifier is not included. We conducted a laboratory experiment to determine the influence of 10 commercial B fertilizer sources in factorial combination with B concentrations ranging from 0 to 4 lb/100 gal (4.8 g·L-1) on solution pH values of distilled water and two natural waters. Two boric acid-based compounds produced acidic reactions relative to background water pH at all tested B concentrations. Their pH responses were influenced by initial water composition. Seven B products produced moderately to strongly alkaline reactions at all but the highest B concentration, regardless of the form of B (polyborate vs. boric acid) initially present in the formulated products. One polyborate product formulated with an acidifier showed intermediate pH behavior. The dependence of solution pH on B rate of the polyborate-containing products was identical in all three waters. The maximum pH values generated by all products occurred in the B concentration range <0.1 to 0.25 lb/100 gal (0.12 to 0.3 g·L-1). Solution pH values declined with increasing B concentration above this range. The pH responses qualitatively conformed to known aqueous chemical behavior of B and the product additives. The complexity of the interaction between initial water chemistry, B concentration, and B fertilizer product reinforces the need to measure the pH of B-amended spray water before adding pH-sensitive compounds.

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Surface Water Quality Assessment from a Mining Area. Case study, Groapa Burlacu lake, Targu Ocna, Romania

Revista de Chimie ◽

10.37358/rc.19.10.7622 ◽

2019 ◽

Vol 70 (10) ◽

pp. 3678-3680

Author(s):

Alina Cochiorca ◽

Narcis Barsan ◽

Florin Marian Nedeff ◽

Ion Sandu ◽

Emilian Florin Mosnegutu ◽

...

Keyword(s):

Water Quality ◽

Surface Water ◽

Surface Water Quality ◽

Water Quality Assessment ◽

Mining Area ◽

Physical Parameters ◽

Mining Activities ◽

Quality Of Water ◽

Nacl Concentration ◽

Water Ph

This paper presents a study on assessment of water quality. According to a study, mining activities have a significant impact on water quality (lakes, surface water and groundwater), which has become a major problem globally. Due to mining and exploitation processes, lakes can be formed around these mines. Also, these lakes have been formed around the world and are steadily increasing. The purpose of this study is to watch the quality of water from the area around mining activities. This study refers to the, Groapa Burlacu lake around the mining exploitation Targu Ocna, Romania. This lake was formed on the northern bottle of the massive salt, strongly affected by the underground activities. Sampling for the determination the concentrations of Cl- and NaCl from the studied area was made at different depths (0 m, -5 m, -10 m, -15 m, -20 m, -25 m, -30 m, -35 m -40 m). Besides these concentrations, physical parameters of the water (pH, turbidity, electrical conductivity, dissolved oxygen and temperature) were also measured. To determine the physical parameters in the monitored area, sampling was done from four different points of the area and then put together for analysis. These parameters were measured on site using portable equipment. The data on the analyzed concentrations indicate that at depths of less than 5.0 m, the NaCl concentration values are more than 250 g/L.

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Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

Scientific Reports ◽

10.1038/s41598-021-81556-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhonghui Thong ◽

Jolena Ying Ying Tan ◽

Eileen Shuzhen Loo ◽

Yu Wei Phua ◽

Xavier Liang Shun Chan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Sensitivity Threshold ◽

Ann Model ◽

Blood Samples ◽

Ann Models ◽

Artificial Neural ◽

The Impact ◽

Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

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Development of a Low-Cost pH Meter for Liquid Chemical Fertilizers

Turkish Journal of Agriculture - Food Science and Technology ◽

10.24925/turjaf.v8i4.840-846.2911 ◽

2020 ◽

Vol 8 (4) ◽

pp. 840-846

Author(s):

Muhittin Yağmur Polat ◽

Abdullah Beyaz ◽

İbrahim Çilingir

Keyword(s):

Ph Value ◽

Low Cost ◽

Ph Sensor ◽

Field Application ◽

Average Error ◽

Chemical Fertilizers ◽

Modern Agriculture ◽

Ph Values ◽

Water Ph ◽

Liquid Chemical

Liquid chemical fertilizers are widely used in modern agriculture. The pH values of these fertilizers are important in terms of their effectiveness in the field application and their effect on the soil-water pH balance. Therefore, it is necessary to measure the pH and other properties of liquid fertilizers with fast, practical and inexpensive methods. With the advancing technology, pH value has been started to measure more accurately and efficiently with the help of low-cost devices. In this study, a low-cost pH meter was developed using a low-cost pH sensor, an Arduino UNO R3 microcontroller board, and a software written in the C/C++ programming language. The developed pH meter was used to measure the pH values of liquid chemical fertilizers. Close variations were observed between the values obtained from it and the values measured by an accredited, laboratory-type pH meter. According to the pH measurements of the two pH meters, R2 ranged 88.1% to 99.3%, average error range 0.14 to 0.28, and average error percentage ranged 1.56% to 6.81% for three different types of liquid fertilizers. In light of these results, it was shown that the developed low-cost pH meter can be useful for practical pH measurement applications.

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Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather: A Case of Rawalpindi Pakistan

Sustainability ◽

10.3390/su131810164 ◽

2021 ◽

Vol 13 (18) ◽

pp. 10164

Author(s):

Ahsen Maqsoom ◽

Bilal Aslam ◽

Muhammad Ehtisham Gul ◽

Fahim Ullah ◽

Abbas Z. Kouzani ◽

...

Keyword(s):

Construction Material ◽

Pulse Velocity ◽

Ann Model ◽

Quadratic Regression ◽

Split Tensile Strength ◽

Curing Conditions ◽

Curing Methods ◽

Ann Models ◽

Hot Weather ◽

The Individual

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.

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Controls on pH in surface waters of northwestern European shelf seas

Biogeosciences Discussions ◽

10.5194/bgd-11-943-2014 ◽

2014 ◽

Vol 11 (1) ◽

pp. 943-974 ◽

Cited By ~ 4

Author(s):

V. M. C. Rérolle ◽

M. Ribas-Ribas ◽

V. Kitidis ◽

I. Brown ◽

D. C. E. Bakker ◽

...

Keyword(s):

Biological Activity ◽

Surface Water ◽

Irish Sea ◽

Carbonate Chemistry ◽

Ph Distribution ◽

European Shelf ◽

Shelf Seas ◽

Water Ph ◽

The Impact ◽

Chemistry Dynamics

Abstract. We present here a high resolution surface water pH dataset obtained in the Northwest European shelf seas in summer 2011. This is the first time that pH has been measured at such a high spatial resolution (10 measurements h–1) in this region. The aim of our paper is to investigate the carbonate chemistry dynamics of the surface water using pH and ancillary data. The main processes controlling the pH distribution along the ship's transect, and their relative importance, were determined using a statistical approach. The study highlights the impact of biological activity, temperature and riverine inputs on the carbonate chemistry dynamics of the shelf seas surface water. For this summer cruise, the biological activity formed the main control of the pH distribution along the cruise transect. Variations in chlorophyll and nutrients explained 29% of the pH variance along the full transect and as much as 68% in the northern part of the transect. In contrast, the temperature distribution explained ca. 50% of the pH variation in the Skagerrak region. Riverine inputs were evidenced by high dissolved organic carbon (DOC) levels in the Strait of Moyle (northern Irish Sea) and the southern North Sea with consequent remineralisation processes and a reduction in pH. The DOC distribution described 15% of the pH variance along the full transect. This study highlights the high spatial variability of the surface water pH in shelf seawaters where a range of processes simultaneously impacts the carbonate chemistry.

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Assessing efficiency of pre-ammonization aimed at reducing carcinogenic risks caused by trihalomethanes in drinking water

Health Risk Analysis ◽

10.21668/health.risk/2020.3.08.eng ◽

2020 ◽

pp. 70-77

Author(s):

L.A. Deryabkina ◽

◽

B.I. Marchenko ◽

N.K. Plugotarenko ◽

A.I. Yukhno ◽

...

Keyword(s):

Drinking Water ◽

Surface Water ◽

Natural Water ◽

Water Source ◽

Water Disinfection ◽

Residual Chlorine ◽

Gas Liquid Chromatography ◽

Initial Water ◽

Organic Chlorine ◽

Deep Purification

In most Russian regions there is still a pressing issue related to providing population with high quality and safe drinking water. Up to now, chlorination has been the primary technique applied to disinfect drinking water as it is highly efficient, reliable, and relatively cheap. However, when chlorine is used to disinfect natural water that contains organic pollutants, it results in risks of by-products occurrence. These products are trihalomethanes, epigenetic carcinogenesis promoters that cause elevated carcinogenic risks under oral, inhalation, and subcutaneous exposure. Our research goal was to hygienically assess efficiency of pre-ammonization applied in water treatment procedures in order to prevent occurrence of carcinogenic organic chlorine compounds during chlorination and to minimize carcinogenic risks. We determined trihalomethanes and residual chlorine contents in model samples of natural water taken from a surface water source after chlorination with different doses of chlorine. We examined 52 pair parallel samples that had undergone pre-ammonization with ammonia sulfate and control ones. Trihalomethanes concentrations were determined in model water samples with gas-liquid chromatography. Basing on the results obtained via experiments on laboratory chlorination of river water, we determined quantitative characteristics and built regression models showing dependence between concentrations of organic chlorine compounds occurring due to chlorination (chloroform, dichlorobrommethane, dibromchloromethane) and chlorine doses and preammonization parameters. It was established that pre-ammonization was the most efficient in terms of preventing trihalomethanes occurrence under such disinfection modes when contents of residual active chlorine didn’t exceed recommended levels (0.8–1.2 mg/L). Basic ways to minimize carcinogenic risks caused by trihalomethanes are systemic control over their contents in drinking water during social and hygienic monitoring procedures; preliminary ammonization of water taken from surface water sources; prevention of unjustified hyper-chlorination; preliminary deep purification of initial water; disinfection with ultrasound radiation instead of preliminary chlorination; etc.

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Application of artificial neural network to control the coagulant dosing in water treatment plant

Water Science & Technology ◽

10.2166/wst.2000.0410 ◽

2000 ◽

Vol 42 (3-4) ◽

pp. 403-408 ◽

Cited By ~ 8

Author(s):

R.-F. Yu ◽

S.-F. Kang ◽

S.-L. Liaw ◽

M.-c. Chen

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Water Treatment ◽

Regression Model ◽

Treatment Plant ◽

Water Treatment Plant ◽

Ann Model ◽

Coagulant Dosage ◽

Ann Models

Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.

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