The Role of PHREEQC Model and Sensor Analysis in Chemical Coagulation Processes Supported by Online Sensors

Year 2024, Volume: 9 Issue: 1, 45 - 52

Meltem Sarp Akarsu Sevgi Tokgöz Güneş

https://doi.org/10.35229/jaes.1407452

Abstract

Population growth and industrial development have led to an increasing demand for water and wastewater treatment in Turkey and around the world. To ensure sustainable treatment, it is necessary to have real-time control and monitor the system. Therefore, this study aims to reveal the removal mechanism and control of the coagulation process using the PHREEQC modeling software, which has a promising potential for simulating the chemical equilibrium and reactions of water. The sensor effectiveness determined by the model was confirmed by experimental tests in the laboratory. This was done to identify the shortcomings and differences of the model, to understand and develop mechanistic structure. To observe the effects of temperature changes in the treatment, PHREEQC software was run for each of the temperatures (T) 1, 9, and 25.3oC, with the addition of FeCl3. The data obtained from pH, conductivity, temperature, and Eh sensors were evaluated. As a result of the study, it was found that different temperatures affect the solubility of the ions, with higher temperatures leading to increased solubility and conductivity. With increasing temperature, the solubility of oxygen in water decreases, while pH, Cl-, and the precipitate Fe(OH)3 are not affected by the temperature change. In general, the modeling results are in line with the analytical results of the samples taken in the laboratory. This highlights the attractiveness of using online sensors for sustainable wastewater treatment. PHREEQC has produced more reliable results by using actual chemical equilibrium constants as it considers equilibrium conditions and includes the effects of ionic bonds and ion pairs.

Keywords

watewater treatment, sustainability, online sensor, modeling, coagulation

Thanks

The authors would like to thank to Cihan GÜNEŞ for modeling support.

Çevrimiçi Sensörler Tarafından Desteklenen Kimyasal Koagülasyon Süreçlerinde PHREEQC Modeli ve Sensör Analizinin Rolü

Abstract

Nüfus artışı ve sanayinin gelişmesine bağlı olarak, ülkemizde ve dünyada su ve atıksu arıtımı gün geçtikçe daha önemli bir sorun haline gelmektedir. Bu durumda arıtma sisteminin tam zamanlı kontrol ve izlenebilirliği ile sürdürülebilir arıtma sağlayabilmek için sensör ve yazılımların önemi artmaktadır. Dolayısıyla bu çalışma kapsamında suyun kimyasal dengesini ve reaksiyonlarını simüle etmekte umut vaat eden PHREEQC modelleme yazılımı kullanılarak koagülasyon prosesinin çalışma mekanizması ve kontrol yapısı ortaya koyulmuştur. Bu amaçla pH, iletkenlik, sıcaklık gibi direkt sensörler ve kimyasal analizler kullanılarak. PHREEQC modeli ile belirlenen sensör etkinliği, laboratuvar ortamında alınan örneklere yapılan deneysel testlerden elde edilen verilerle onaylanarak, eksiklikleri ve farklılıkları belirlenmiş ve mekanistik yapının anlaşılması ve geliştirilmesi amaçlanmıştır. Mevsimsel sıcaklık farklılıkları dikkate alınarak arıtmada sıcaklık değişiminin neden olduğu etkilerin gözlemlenmesi amaçlanmış, bunun için, 1, 9 ve 25,3oC sıcaklıklarının (T) her biri için, FeCl3 koagülantı eklenerek PHREEQC yazılımı çalıştırılmış, pH, iletkenlik, sıcaklık, Eh sensörlerinden elde edilen veriler değerlendirilmiştir. Çalışma sonucunda farklı sıcaklıkların iyon çözünürlüğünü etkilediği, daha yüksek sıcaklıklarda iyon çözünürlüğü arttığı ve buna bağlı olarak da iletkenlik arttığı gözlemlenmiştir. Sıcaklık artışı oksijenin sudaki çözünürlüğünü azaltmakta, pH, Cl- ve çökebilen Fe(OH)3 parametreleri sıcaklık değişiminden etkilenmemektedir. Genel olarak modelleme sonuçları, laboratuvar ortamından alınan örneklere ait analiz sonuçları ile paralellik göstermektedir. Bu durum çevrimiçi sensörlerin kullanımının sürdürülebilir atıksu arıtımı için cazip olduğunu vurgulamaktadır. PHREEQC, denge koşullarını göz önünde bulundurması ve iyonik bağ ile iyon çiftlerinin etkilerini dahil etmesi sayesinde güncel kimyasal denge sabitlerini kullanarak daha güvenilir sonuçlar ortaya koymuştur.

Keywords

atıksu arıtımı, sürdürülebilirlik, çevrimiçi sensör, modelleme, koagülasyon

References

• Burnet, J.B., Sylvestre, É., Jalbert, J., Imbeault, S., Servais, P., Prévost, M. & Dorner, S. (2019). Tracking the contribution of multiple raw and treated wastewater discharges at an urban drinking water supply using near real-time monitoring of β-d-glucuronidase activity. Water research, 164, 114869.
• Corbella, C., Hartl, M., Fernandez-Gatell, M. & Puigagut, J. (2019). MFC-based biosensor for domestic wastewater COD assessment in constructed wetlands. The Science of the total environment, 660, 218-226.
• Cravotta, C.A. (2021). Interactive PHREEQ-N- AMDTreat water-quality modeling tools to evaluate performance and design of treatment systems for acid mine drainage. Applied Geochemistry, 126, 104845.
• Crini, G. & Lichtfouse, E. (2019). Advantages and disadvantages of techniques used for wastewater treatment. Environmental Chemistry Letters, 17, 145-155.
• Dutta, D., Arya, S. & Kumar, S. (2021). Industrial wastewater treatment: Current trends, bottlenecks, and best practices. Chemosphere, 285, 131245.
• Ellgen, P. (2023). 5: Chemical kinetics, reaction mechanisms, and chemical equilibrium. Access date: 01.11.2023, https://chem.libretexts.org/Bookshelves/Physica l_and_Theoretical_Chemistry_Textbook_Maps/ Thermodynamics_and_Chemical_Equilibrium_( Ellgen)/05%3A_Chemical_Kinetics_Reaction_ Mechanisms_and_Chemical_Equilibrium
• Garfí, M., Pedescoll, A., Carretero, J., Puigagut, J. & García, J. (2014). Reliability and economic feasibility of online monitoring of constructed wetlands performance. Desalination and Water Treatment, 52, 5848-5855.
• Haimi, H., Mulas, M. & Vahala, R. (2010). Process automation in Wastewater Treatment Plants: the Finnish experience. European Water Association (EWA).
• Huang, C. & Liu, C. (1996). Automatic Control For Chemical Dosing İn Laboratory-Scale Coagulation Process By Using An Optical monitor. Water Research, 30(8), 1924-1929.
• Jeppsson, U., Alex, J., Pons, M.N., Spanjers, H. & Vanrolleghem, P.A. (2002). Status and future trends of ICA in wastewater treatment - A European perspective. Water science and technology: a journal of the International Association on Water Pollution Research, 45(4- 5), 485-494.
• Kim, C.M. (2017). Coagulant Dosage Determination Using Neural Networks and ANFIS in Drinking Water Treatment Plant. Asian Institute of Technology School of Engineering and Technology, Thailand.
• Lázaro Gil, J., van den Brink, P., De Moel, P., van der Steen, P. & R Rene, E. (2022). Homogeneous ferrous iron oxidation in a pilot-scale electrocoagulation system treating municipal wastewater: a model validation and simulation study. Water science and technology: a journal of the International Association on Water Pollution Research, 86(10), 2555-2569. DOI: 10.2166/wst.2022.343.
• Manamperuma, L., Wei, L. & Ratnaweera, H. (2017). Multi-parameter based coagulant dosing control. Water science and technology: a journal of the International Association on Water Pollution Research, 75(9-10), 2157-2162. DOI: 10.2166/wst.2017.058.
• Morin-Crini, N. & Crini, G. (2017). Eaux industrielles contaminées, Presses universitaires de Franche- Comté, Besançon.
• Nordstrom, D.K. (1977). Thermochemical redox equilibria of ZoBell’s solution. Geochim. Cosmochim. Acta, 41, 1835–1841.
• Papias, S., Masson, M., Pelletant, S., Prost-Boucle, S. & Boutin, C. (2018). In situ continuous monitoring of nitrogen with ion-selective electrodes in a constructed wetland receiving treated wastewater: an operating protocol to obtain reliable data. Water science and technology: a journal of the International Association on Water Pollution Research, 77(5- 6), 1706-1713.
• Parkhurst, D.L. & Appelo, C.A.J. (2013). Description of Input and Examples for PHREEQC Version 3 - A Computer Program for Speciation, Batch- Reaction, One-Dimensional Transport, and Inverse Geochemical Calculations. US Geological Survey Techniques and Methods, 6(A43), 497.
• Ratnaweera, H. & Fettig, J. (2015). State of the art of online monitoring and control of the coagulation process. Water, 7, 6574-6597.
• Rizzo, A., Bresciani, R., Martinuzzi, N. & Masi, F. (2020). Online monitoring of a long-term full- scale constructed wetland for the treatment of winery wastewater in Italy. Applied Science, 10, 555.
• Stefanakis, A.I. (2020). Constructed wetlands for sustainable wastewater treatment in hot and arid climates: opportunities, challenges and case studies in the Middle East. Water, 12, 1665.
• Thorstenson, D.C. (1984). The concept of electron activity and its relation to redox potentials in aqueous geochemical systems. U.S. Geological Survey, 10.3133/ofr8472.
• Valentin N. & Denœux, T. (2000). A neural network- based software sensor for coagulation control in a water treatment plant. Intelligent Data Analysis, 5, 23–39. https://www.hds.utc.fr/~tdenoeux/dokuwiki/_m edia/en/revues/ida00040.pdf
• van der Helm, A.W.C., Kramer, O.J.I, Hooft, J.F.M. & de Moel, P.J. (2015). Plant wide chemical water stability modelling with PHREEQC for drinking water treatment. Conference: IWC International Water Conferences, 2nd New Developments In IT & Water, Rotterdam, the Netherlands.
• Wibisono, R.P., Rusmin, P.H. & Notodarmojo, S. (2020). Optimization Coagulation Process of Water Treatment Plant Using Neural Network and Internet of Things (IoT) Communication. 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). DOI: 10.1109/ISRITI51436.2020.9315417.