- Email: [email protected]
Assessment of physical and chemical indicators on water turbidity
Physica A 527 (2019) 121171
Contents lists available at ScienceDirect
Physica A journal homepage: www.elsevier.com/locate/physa
Assessment of physical and chemical indicators on water turbidity Dragoljub Miljojkovic a , Ivana Trepsic b , Milos Milovancevic c ,
∗
a
Public company Srbijavode WMC ‘‘Morava" Trg kralja Aleksandra Ujedinitelja 2, 18000 Niš, Serbia Water Company "Erozija" d.o.o. Niš ul. General Milojka Lešjanina no.12, 18 000 Niš, Serbia c University of Nis, Faculty of Mechanical Engineering, Aleksandra Medvedeva 14, 18000 Nis, Serbia b
highlights • • • •
To analyze water turbidity by data mining approach. Experimental investigation was made to acquire data for the statistical analysis. The data represents the chemical and physical properties of the clean water. To maintain the quality of the clean water.
article
info
Article history: Received 28 March 2019 Received in revised form 17 April 2019 Available online 25 April 2019 Keywords: Clean water Chemical Physical Data mining Statistics
a b s t r a c t The main aim of the study was to effects evaluation of physical and chemical indicators on water turbidity by data mining algorithm. In order to perform the evaluation experimental measurements were performed to acquire data for the statistical analysis. The data represents the physical and chemical properties of clean water. Adaptive neuro fuzzy inference system (ANFIS) was used as data mining algorithm to determine the effects of chemical and physical indicators on water turbidity. The obtained results could be used to improve and to maintain the quality of the clean water. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Water turbidity evaluation and monitoring is necessary in many cases in order to ensure high quality of the water. However, evaluation of the turbidity is not enough emphasized and there is need for water turbidity characterization in more fashioned way. The turbidity could cause serious problems in drinking water treatment [1]. Turbidity is not a contaminant concentration but is a property that represents the ‘‘sum’’ of other contaminants, with the advantage that it can be cheaper and easily measured than biological oxygen demand, chemical oxygen demand, suspended solids, dissolved solids, among others [2]. Turbidity of source water may be principal indicator in characterizing the water filter’s lifetime in terms of water production capacity [3]. Performance of graphene oxide (GO) as a coagulant in turbidity removal from naturally and artificially turbid raw surface water was presented in paper [4] and the outcomes of the study highlight the excellent coagulation performance of GO for the removal of turbidity and biological contaminants from raw surface water. In paper [5] was found that the concentration of iron species and ultrasonic waves affects the intensity of the turbidity. In study [6] was investigated shallow turbidity density currents and underflows from mechanical ∗ Corresponding author. E-mail address: [email protected] (M. Milovancevic). https://doi.org/10.1016/j.physa.2019.121171 0378-4371/© 2019 Elsevier B.V. All rights reserved.
2
D. Miljojkovic, I. Trepsic and M. Milovancevic / Physica A 527 (2019) 121171 Table 1 Input parameters. 1. Water level 2. Flow 3. Water temperature
7–283 0.08–348 0.1–35
Physical parameters
4. Suspended matters 5. Dissolved oxygen (DO) 6. Oxygen saturation 7. Alkalinity 8. Total hardness CaCO3 9. CO2 10. Carbonates - CO3 – 11. Bicarbonates - HCO3 12. Total Alkalinity - CaCO3 13. PH 14. El. conductivity, mS/cm 15. Total dissolved salts 16. Ammonia, NH3, mg/l 17. Nitrites, NO2-, mg/l 18. Nitrates, NO3-, mg/l 19. Organic nitrogen 20. Total nitrogen, N, mg/l/Kjeldal 21. Orthophosphates (PO4-P) 22. Total phosphorus, P, mg/l
0.1–367 1.9–16.4 24–163 2.1–11.7 126–899 0–13.6 0–35.4 81–715 105–586 7.1–8.9 233–1478 133–1252 0–2 0.001–0.6 0.002–3.8 0.001–4.8 0.13–7.5 0.001–0.56 0.001–1390
Chemical parameters
point of view where a simple hyperbolic model for such flows was proposed. The higher the water turbidity, and the longer the period of deposition, the less the flow rate of groundwater recharge [7]. A disposable instrument for measuring water turbidity in rivers and coastal oceans was described in paper [8]. Results in article [9] were shown a clear strong connection between the Danube discharge and water turbidity in the coastal area. Experimental tests in article [10] were performed to evaluate the effects of turbidity concentration, coagulant quantity, water pH, and humic acid concentration on the coagulation of water turbidity by a FeCl3 -induced crude extract (FCE). River turbidity is of dynamic nature, and its stable state is significantly changed during the period of heavy rainfall events [11]. Although there are different approaches for the analyzing of water turbidity in this study the main aim is to perform the sensitivity analysis of the water turbidity based on physical and chemical characteristics. For the approach data mining algorithm, namely, adaptive neuro-fuzzy inference system (ANFIS) [12–14], was used. 2. Methodology The quality of the water depends on three indicators: physical, chemical and biological. Analyzing these parameters determines the water quality and turbidity, that is, the level of pollution because the water quality standards define the quantitative and qualitative number of allowed particles. Running water turbidity tests are usually performed before and after a settled site to determine the impact of the populated area on water pollution. In our research data on water turbidity upstream and downstream of the populated place were used. The aim of this paper is to evaluate the water turbidity indicators using ANFIS. 2.1. Experimental investigation Physical and chemical parameters of the clean water are tracked and acquired in order to maintain the water quality in Nis, Serbia. All of the samples for investigation represent the crude water and it was investigated according to official methodology. During investigation of chemical characteristics of water 1000 samples are acquired. Table 1 presents the acquired data for the water characteristics. The chemical and physical characteristics are used as the input parameters in the ANFIS model. Water transparency or turbidity measured in Nephelometric turbidity units (NTU) (25-1110) is used ad ANFIS output. Water turbidity is make of suspended inorganic substance, organic substance, microscopic organisms and etc. 2.2. ANFIS methodology ANFIS network has 5 layers. Layer 1 receives the inputs and convert them in the fuzzy value by membership functions. In this study bell shaped membership function was used since the function has the highest capability for the regression of the nonlinear data. Second layer multiplies the fuzzy signals from the first layer and provides the firing strength of as rule. The third layer is the rule layers where all signals from the second layer are normalized. The fourth layer provides the inference of rules and all signals are converted in crisp values. The final layers summarized the all signals and provided the output crisp value. Results of the ANFIS models were presented as root means square error (RMSE)
D. Miljojkovic, I. Trepsic and M. Milovancevic / Physica A 527 (2019) 121171
3
Table 2 Single parameter effect on drinking water (trntraining, chk-checking)
Fig. 1. Single parameter effect on water turbidity.
3. Results 3.1. Sensitivity analysis of chemical characteristics Sensitivity analysis of the chemical and physical characteristics of the clean water is performed in order to determine the effects of the chemical and physical parameters on the water turbidity. Neuro fuzzy network is trained for each input to estimate their root means square errors (RMSE). According the results the parameter suspended matters (input 4) has the smallest RMSE value (Table 2 and Fig. 1). Hence the parameter has the highest influence on the water turbidity. In other words any small variation of suspended matters will produce the highest variation of water turbidity. On the other hand the parameter 22 has the smallest effect on the water turbidity. If one combine two parameters the optimal combination which has the highest influence on the water turbidity is combination of suspended matters and oxygen saturation - trn = 70.4441, chk = 100.8751 (Fig. 2). In other words if suspended matters and oxygen saturation are variated in the same time it could produce the high variation of water turbidity. If one combine three parameters the optimal combination which has the highest influence on the water turbidity is combination of flow, suspended matters and PH - trn = 66.5763, chk = 191.5385 (Fig. 3). In other words if flow, suspended matters and PH are variated in the same time it could produce the high variation of water turbidity.
4
D. Miljojkovic, I. Trepsic and M. Milovancevic / Physica A 527 (2019) 121171
Fig. 2. Two parameters effect on water turbidity.
Fig. 3. Three parameters effect on water turbidity.
4. Conclusion Analyzing of water turbidity could be complex task due to the many chemical and physical factors. Therefore in this study was applied a data mining algorithm to overcome the difficulties by removing some unnecessary input parameters. ANFIS methodology was used for the analyses in order to perform the ranking of the input parameters based on their influence on the water turbidity. References [1] L. Parra, J. Rocher, J. Escrivá, J. Lloret, Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms, Aquac. Eng. (2018). [2] C.S. Lee, Y.C. Lee, H.M. Chiang, Abrupt state change of river water quality (turbidity): Effect of extreme rainfalls and typhoons, Sci. Total Environ. 557 (2016) 91–101.
D. Miljojkovic, I. Trepsic and M. Milovancevic / Physica A 527 (2019) 121171
5
[3] C. Salvinelli, A.C. Elmore, M.R. Reidmeyer, K.D. Drake, K.I. Ahmad, Characterization of the relationship between ceramic pot filter water production and turbidity in source water, Water Res. 104 (2016) 28–33. [4] A.E. Aboubaraka, E.F. Aboelfetoh, E.Z.M. Ebeid, Coagulation effectiveness of graphene oxide for the removal of turbidity from raw surface water, Chemosphere 181 (2017) 738–746. [5] N. Villota, J.M. Lomas, L.M. Camarero, Effect of ultrasonic waves on the water turbidity during the oxidation of phenol. formation of (hydro) peroxo complexes, Ultrason. Sonochemistry 39 (2017) 439–445. [6] V. Liapidevskii, D. Dutykh, M. Gisclon, On the modelling of shallow turbidity flows, 2017, arXiv preprint arXiv:1704.08024. [7] A. Azis, H. Yusuf, Z. Faisal, M. Suradi, Water turbidity impact on discharge decrease of groundwater recharge in recharge reservoir, Procedia Eng. 125 (2015) 199–206. [8] R. Marchant, D. Reading, J. Ridd, S. Campbell, P. Ridd, A drifter for measuring water turbidity in rivers and coastal oceans, Marine Pollut. Bull. 91 (1) (2015) 102–106. [9] S. Constantin, D. Doxaran, Ş. Constantinescu, Estimation of water turbidity and analysis of its spatio-temporal variability in the Danube River plume (Black Sea) using MODIS satellite data, Cont. Shelf Res. 112 (2016) 14–30. [10] B. Ramavandi, Treatment of water turbidity and bacteria by using a coagulant extracted from Plantago ovata, Water Resour. Ind. 6 (2014) 36–50. [11] C.S. Lee, Y.C. Lee, H.M. Chiang, Abrupt state change of river water quality (turbidity): Effect of extreme rainfalls and typhoons, Sci. Total Environ. 557 (2016) 91–101. [12] J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference systems, IEEE Trans. Syst. Man Cybern. 23 (1993) 665–685. [13] D. Petković, Ž. Ćojbašič, V. Nikolić, Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation, Renew. Sustain. Energy Rev. 28 (2013) 191–195. [14] D. Petković, Ž. Ćojbašić, V. Nikolić, S. Shamshirband, M.L.M. Kiah, N.B. Anuar, A.W.A. Wahab, Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission, Energy 64 (2014) 868–874.
Contents lists available at ScienceDirect
Physica A journal homepage: www.elsevier.com/locate/physa
Assessment of physical and chemical indicators on water turbidity Dragoljub Miljojkovic a , Ivana Trepsic b , Milos Milovancevic c ,
∗
a
Public company Srbijavode WMC ‘‘Morava" Trg kralja Aleksandra Ujedinitelja 2, 18000 Niš, Serbia Water Company "Erozija" d.o.o. Niš ul. General Milojka Lešjanina no.12, 18 000 Niš, Serbia c University of Nis, Faculty of Mechanical Engineering, Aleksandra Medvedeva 14, 18000 Nis, Serbia b
highlights • • • •
To analyze water turbidity by data mining approach. Experimental investigation was made to acquire data for the statistical analysis. The data represents the chemical and physical properties of the clean water. To maintain the quality of the clean water.
article
info
Article history: Received 28 March 2019 Received in revised form 17 April 2019 Available online 25 April 2019 Keywords: Clean water Chemical Physical Data mining Statistics
a b s t r a c t The main aim of the study was to effects evaluation of physical and chemical indicators on water turbidity by data mining algorithm. In order to perform the evaluation experimental measurements were performed to acquire data for the statistical analysis. The data represents the physical and chemical properties of clean water. Adaptive neuro fuzzy inference system (ANFIS) was used as data mining algorithm to determine the effects of chemical and physical indicators on water turbidity. The obtained results could be used to improve and to maintain the quality of the clean water. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Water turbidity evaluation and monitoring is necessary in many cases in order to ensure high quality of the water. However, evaluation of the turbidity is not enough emphasized and there is need for water turbidity characterization in more fashioned way. The turbidity could cause serious problems in drinking water treatment [1]. Turbidity is not a contaminant concentration but is a property that represents the ‘‘sum’’ of other contaminants, with the advantage that it can be cheaper and easily measured than biological oxygen demand, chemical oxygen demand, suspended solids, dissolved solids, among others [2]. Turbidity of source water may be principal indicator in characterizing the water filter’s lifetime in terms of water production capacity [3]. Performance of graphene oxide (GO) as a coagulant in turbidity removal from naturally and artificially turbid raw surface water was presented in paper [4] and the outcomes of the study highlight the excellent coagulation performance of GO for the removal of turbidity and biological contaminants from raw surface water. In paper [5] was found that the concentration of iron species and ultrasonic waves affects the intensity of the turbidity. In study [6] was investigated shallow turbidity density currents and underflows from mechanical ∗ Corresponding author. E-mail address: [email protected] (M. Milovancevic). https://doi.org/10.1016/j.physa.2019.121171 0378-4371/© 2019 Elsevier B.V. All rights reserved.
2
D. Miljojkovic, I. Trepsic and M. Milovancevic / Physica A 527 (2019) 121171 Table 1 Input parameters. 1. Water level 2. Flow 3. Water temperature
7–283 0.08–348 0.1–35
Physical parameters
4. Suspended matters 5. Dissolved oxygen (DO) 6. Oxygen saturation 7. Alkalinity 8. Total hardness CaCO3 9. CO2 10. Carbonates - CO3 – 11. Bicarbonates - HCO3 12. Total Alkalinity - CaCO3 13. PH 14. El. conductivity, mS/cm 15. Total dissolved salts 16. Ammonia, NH3, mg/l 17. Nitrites, NO2-, mg/l 18. Nitrates, NO3-, mg/l 19. Organic nitrogen 20. Total nitrogen, N, mg/l/Kjeldal 21. Orthophosphates (PO4-P) 22. Total phosphorus, P, mg/l
0.1–367 1.9–16.4 24–163 2.1–11.7 126–899 0–13.6 0–35.4 81–715 105–586 7.1–8.9 233–1478 133–1252 0–2 0.001–0.6 0.002–3.8 0.001–4.8 0.13–7.5 0.001–0.56 0.001–1390
Chemical parameters
point of view where a simple hyperbolic model for such flows was proposed. The higher the water turbidity, and the longer the period of deposition, the less the flow rate of groundwater recharge [7]. A disposable instrument for measuring water turbidity in rivers and coastal oceans was described in paper [8]. Results in article [9] were shown a clear strong connection between the Danube discharge and water turbidity in the coastal area. Experimental tests in article [10] were performed to evaluate the effects of turbidity concentration, coagulant quantity, water pH, and humic acid concentration on the coagulation of water turbidity by a FeCl3 -induced crude extract (FCE). River turbidity is of dynamic nature, and its stable state is significantly changed during the period of heavy rainfall events [11]. Although there are different approaches for the analyzing of water turbidity in this study the main aim is to perform the sensitivity analysis of the water turbidity based on physical and chemical characteristics. For the approach data mining algorithm, namely, adaptive neuro-fuzzy inference system (ANFIS) [12–14], was used. 2. Methodology The quality of the water depends on three indicators: physical, chemical and biological. Analyzing these parameters determines the water quality and turbidity, that is, the level of pollution because the water quality standards define the quantitative and qualitative number of allowed particles. Running water turbidity tests are usually performed before and after a settled site to determine the impact of the populated area on water pollution. In our research data on water turbidity upstream and downstream of the populated place were used. The aim of this paper is to evaluate the water turbidity indicators using ANFIS. 2.1. Experimental investigation Physical and chemical parameters of the clean water are tracked and acquired in order to maintain the water quality in Nis, Serbia. All of the samples for investigation represent the crude water and it was investigated according to official methodology. During investigation of chemical characteristics of water 1000 samples are acquired. Table 1 presents the acquired data for the water characteristics. The chemical and physical characteristics are used as the input parameters in the ANFIS model. Water transparency or turbidity measured in Nephelometric turbidity units (NTU) (25-1110) is used ad ANFIS output. Water turbidity is make of suspended inorganic substance, organic substance, microscopic organisms and etc. 2.2. ANFIS methodology ANFIS network has 5 layers. Layer 1 receives the inputs and convert them in the fuzzy value by membership functions. In this study bell shaped membership function was used since the function has the highest capability for the regression of the nonlinear data. Second layer multiplies the fuzzy signals from the first layer and provides the firing strength of as rule. The third layer is the rule layers where all signals from the second layer are normalized. The fourth layer provides the inference of rules and all signals are converted in crisp values. The final layers summarized the all signals and provided the output crisp value. Results of the ANFIS models were presented as root means square error (RMSE)
D. Miljojkovic, I. Trepsic and M. Milovancevic / Physica A 527 (2019) 121171
3
Table 2 Single parameter effect on drinking water (trntraining, chk-checking)
Fig. 1. Single parameter effect on water turbidity.
3. Results 3.1. Sensitivity analysis of chemical characteristics Sensitivity analysis of the chemical and physical characteristics of the clean water is performed in order to determine the effects of the chemical and physical parameters on the water turbidity. Neuro fuzzy network is trained for each input to estimate their root means square errors (RMSE). According the results the parameter suspended matters (input 4) has the smallest RMSE value (Table 2 and Fig. 1). Hence the parameter has the highest influence on the water turbidity. In other words any small variation of suspended matters will produce the highest variation of water turbidity. On the other hand the parameter 22 has the smallest effect on the water turbidity. If one combine two parameters the optimal combination which has the highest influence on the water turbidity is combination of suspended matters and oxygen saturation - trn = 70.4441, chk = 100.8751 (Fig. 2). In other words if suspended matters and oxygen saturation are variated in the same time it could produce the high variation of water turbidity. If one combine three parameters the optimal combination which has the highest influence on the water turbidity is combination of flow, suspended matters and PH - trn = 66.5763, chk = 191.5385 (Fig. 3). In other words if flow, suspended matters and PH are variated in the same time it could produce the high variation of water turbidity.
4
D. Miljojkovic, I. Trepsic and M. Milovancevic / Physica A 527 (2019) 121171
Fig. 2. Two parameters effect on water turbidity.
Fig. 3. Three parameters effect on water turbidity.
4. Conclusion Analyzing of water turbidity could be complex task due to the many chemical and physical factors. Therefore in this study was applied a data mining algorithm to overcome the difficulties by removing some unnecessary input parameters. ANFIS methodology was used for the analyses in order to perform the ranking of the input parameters based on their influence on the water turbidity. References [1] L. Parra, J. Rocher, J. Escrivá, J. Lloret, Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms, Aquac. Eng. (2018). [2] C.S. Lee, Y.C. Lee, H.M. Chiang, Abrupt state change of river water quality (turbidity): Effect of extreme rainfalls and typhoons, Sci. Total Environ. 557 (2016) 91–101.
D. Miljojkovic, I. Trepsic and M. Milovancevic / Physica A 527 (2019) 121171
5
[3] C. Salvinelli, A.C. Elmore, M.R. Reidmeyer, K.D. Drake, K.I. Ahmad, Characterization of the relationship between ceramic pot filter water production and turbidity in source water, Water Res. 104 (2016) 28–33. [4] A.E. Aboubaraka, E.F. Aboelfetoh, E.Z.M. Ebeid, Coagulation effectiveness of graphene oxide for the removal of turbidity from raw surface water, Chemosphere 181 (2017) 738–746. [5] N. Villota, J.M. Lomas, L.M. Camarero, Effect of ultrasonic waves on the water turbidity during the oxidation of phenol. formation of (hydro) peroxo complexes, Ultrason. Sonochemistry 39 (2017) 439–445. [6] V. Liapidevskii, D. Dutykh, M. Gisclon, On the modelling of shallow turbidity flows, 2017, arXiv preprint arXiv:1704.08024. [7] A. Azis, H. Yusuf, Z. Faisal, M. Suradi, Water turbidity impact on discharge decrease of groundwater recharge in recharge reservoir, Procedia Eng. 125 (2015) 199–206. [8] R. Marchant, D. Reading, J. Ridd, S. Campbell, P. Ridd, A drifter for measuring water turbidity in rivers and coastal oceans, Marine Pollut. Bull. 91 (1) (2015) 102–106. [9] S. Constantin, D. Doxaran, Ş. Constantinescu, Estimation of water turbidity and analysis of its spatio-temporal variability in the Danube River plume (Black Sea) using MODIS satellite data, Cont. Shelf Res. 112 (2016) 14–30. [10] B. Ramavandi, Treatment of water turbidity and bacteria by using a coagulant extracted from Plantago ovata, Water Resour. Ind. 6 (2014) 36–50. [11] C.S. Lee, Y.C. Lee, H.M. Chiang, Abrupt state change of river water quality (turbidity): Effect of extreme rainfalls and typhoons, Sci. Total Environ. 557 (2016) 91–101. [12] J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference systems, IEEE Trans. Syst. Man Cybern. 23 (1993) 665–685. [13] D. Petković, Ž. Ćojbašič, V. Nikolić, Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation, Renew. Sustain. Energy Rev. 28 (2013) 191–195. [14] D. Petković, Ž. Ćojbašić, V. Nikolić, S. Shamshirband, M.L.M. Kiah, N.B. Anuar, A.W.A. Wahab, Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission, Energy 64 (2014) 868–874.