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Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives

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Abstract

Biological wastewater treatment using algae–bacteria consortia for nutrient uptake and resource recovery is a ‘paradigm shift’ from the mainstream wastewater treatment process to mitigate pollution and promote circular economy. The symbiotic relationship between algae and bacteria is complex in open or closed biological wastewater treatment systems. In this regard, machine learning algorithms (MLAs) have found to be advantageous to predict the uncertain performances of the treatment processes. MLAs have shown satisfactory results for effective real-time monitoring, optimization, prediction of uncertainties and fault detection of complex environmental systems. By incorporating these algorithms with online sensors, the transient operating conditions during the treatment process including disruptions or failures due to leaking pipelines, malfunctioning of bioreactors, unexpected fluctuations of organic loadings, flow rate, and temperature can be forecasted efficiently. This paper reviews the state-of-the-art MLA approaches for the integrated operation of biological wastewater treatment systems combining algal biomass production and nutrient recovery from municipal wastewater.

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Abbreviations

\(NH_{4}^{ + }\) :

Ammonium

\(NO_{3}^{ - }\) :

Nitrate

ANN:

Artificial neural network

ARMA:

Auto-regressive moving average model

BOD:

Biochemical oxygen demand

CNN:

Convolutional neural network

COD:

Chemical oxygen demand

DO:

Dissolved oxygen

FNN:

Fuzzy neural network

FWSF:

Free water surface flow

GPR:

Gaussian progress regression

HRT:

Hydraulic retention time

HSSF:

Horizontal subsurface flow

ML:

Machine learning

MLA:

Machine learning algorithm

MLP:

Multilayer perceptron

MLSS:

Mixed liquor suspended solids

NH3 :

Ammonia

OLR:

Organic loading rate

PCA:

Principal component analysis

QTA:

Qualitative trend analysis

R 2 :

Correlation coefficient

RL:

Reinforcement learning

RMSE:

Root-mean-squared error

SCADA:

Supervisory control and data acquisition

SVM:

Support vector machine

TDS:

Total dissolved solids

TN:

Total nitrogen

TP:

Total phosphorus

TSS:

Total suspended solids

WWTP:

Wastewater treatment plant

References

  • Abdel-Raouf N, Al-Homaidan AA, Ibraheem IBM (2012) Microalgae and wastewater treatment. Saudi J Biol Sci 19(3):257–275

    Article  CAS  Google Scholar 

  • Al-Sakkari EG, Abdeldayem OM, Genina EE, Amin L, Bahgat NT, Rene ER, El-Sherbiny IM (2020) New alginate-based interpenetrating polymer networks for water treatment: a response surface methodology based optimization study. Int J Biol Macromol 155:772–785

    Article  CAS  Google Scholar 

  • Ambat I, Tang WZ, Sillanpää M (2019) Statistical analysis of sustainable production of algal biomass from wastewater treatment process. Biomass Bioenerg 120:471–478

    Article  CAS  Google Scholar 

  • Baroukh C, Muñoz-Tamayo R, Bernard O, Steyer JP (2015) Mathematical modeling of unicellular microalgae and cyanobacteria metabolism for biofuel production. Curr Opin Biotechnol 33:198–205

    Article  CAS  Google Scholar 

  • Becker EW (2007) Micro-algae as a source of protein. Biotechnol Adv 25(2):207–210

    Article  CAS  Google Scholar 

  • Belanche L, Valdés JJ, Comas J, Roda IR, Poch M (2000) Prediction of the bulking phenomenon in wastewater treatment plants. Artif Intell Eng 14(4):307–317

    Article  Google Scholar 

  • Blanken W, Janssen M, Cuaresma M, Libor Z, Bhaiji T, Wijffels RH (2014) Biofilm growth of Chlorella sorokiniana in a rotating biological contactor based photobioreactor. Biotechnol Bioeng 111(12):2436–2445

    Article  CAS  Google Scholar 

  • Bleakley S, Hayes M (2017) Algal proteins: extraction, application, and challenges concerning production. Foods 6(5):33

    Article  Google Scholar 

  • Blumensaat F, Leitão JP, Ort C, Rieckermann J, Scheidegger A, Vanrolleghem PA, Villez K, (2019) How urban storm-and wastewater management prepares for emerging opportunities and threats: digital transformation, ubiquitous sensing, new data sources, and beyond-a horizon scan. Environ Sci Technol 53(15):8488–8498

    Article  CAS  Google Scholar 

  • Bradford E, Schweidtmann AM, Zhang D, Jing K, del Rio-Chanona EA (2018) Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. Comput Chem Eng 118:143–158

    Article  CAS  Google Scholar 

  • Burkov A (2019) The hundred-page machine learning book. Andriy Burkov, NewYork

    Google Scholar 

  • Burman I, Sinha A (2020) Performance evaluation and organic mass balance for treatment of high strength wastewater by anaerobic hybrid membrane bioreactor. Environm Prog Sustain Energy 39(2):e13311

    CAS  Google Scholar 

  • Calderón OAR, Abdeldayem OM, Pugazhendhi A, Rene ER (2020) Current updates and perspectives of biosorption technology: An alternative for the removal of heavy metals from wastewater. Current Pollution Rep 6:1–20

    Google Scholar 

  • Cao X, Xi Y, Liu J, Chu Y, Wu P, Yang M, Xue S (2019) New insights into the CO2-steady and pH-steady cultivations of two microalgae based on continuous online parameter monitoring. Algal Research 38:101370

    Article  Google Scholar 

  • Cardell-Oliver, R., & Sarkar, C. (2016). Robust sensor data collection over a long period using virtual sensing. In: Proceedings of the Workshop on Time Series Analytics and Applications, pp. 2–7.

  • Chen CY, Yeh KL, Aisyah R, Lee DJ, Chang JS (2011) Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: A critical review. Biores Technol 102(1):71–81

    Article  CAS  Google Scholar 

  • Cho DH, Choi JW, Kang Z, Kim BH, Oh HM, Kim HS, Ramanan R (2017) Microalgal diversity fosters stable biomass productivity in open ponds treating wastewater. Sci Rep 7(1):1–11

    Article  Google Scholar 

  • Christenson L, Sims R (2011) Production and harvesting of microalgae for wastewater treatment, biofuels, and bioproducts. Biotechnol Adv 29(6):686–702

    Article  CAS  Google Scholar 

  • Chronakis IS, Madsen M (2011) Algal proteins. In: Handbook of food proteins, pp. 353–394. Woodhead Publishing, UK.

  • Corominas L, Garrido-Baserba M, Villez K, Olsson G, Cortés U, Poch M (2018) Transforming data into knowledge for improved wastewater treatment operation: a critical review of techniques. Environ Modell Softw 106:89–103

    Article  Google Scholar 

  • Corominas L, Villez K, Aguado D, Rieger L, Rosén C, Vanrolleghem PA (2011) Performance evaluation of fault detection methods for wastewater treatment processes. Biotechnol Bioeng 108(2):333–344

    Article  CAS  Google Scholar 

  • Dahman, Y., Syed, K., Begum, S., Roy, P., & Mohtasebi, B. (2019). Biofuels: Their characteristics and analysis. In: Verma D, Fortunati E, Jain S, Zhang X (eds) Biomass, biopolymer-based materials, and bioenergy. Woodhead Publishing, pp 277–325

  • de Assis LR, Calijuri ML, Assemany PP, Berg EC, Febroni LV, Bartolomeu TA (2019) Evaluation of the performance of different materials to support the attached growth of algal biomass. Algal Res 39:101440

    Article  Google Scholar 

  • del Rio-Chanona EA, Ahmed NR, Zhang D, Lu Y, Jing K (2017) Kinetic modelling and process analysis for Desmodesmus sp. lutein photo-production. AIChE J. 63(7):2546–2554

    Article  Google Scholar 

  • del Rio-Chanona EA, Liu J, Wagner JL, Zhang D, Meng Y, Xue S, Shah N (2018) Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production. Biotechnol Bioeng 115(2):359–370

    Article  Google Scholar 

  • del Rio-Chanona EA, Cong X, Bradford E, Zhang D, Jing K (2019) Review of advanced physical and data-driven models for dynamic bioprocess simulation: case study of algae-bacteria consortium wastewater treatment. Biotechnol Bioeng 116(2):342–353

    Article  Google Scholar 

  • del Rio-Chanona EA, Wagner JL, Ali H, Fiorelli F, Zhang D, Hellgardt K (2019) Deep learning-based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design. AIChE J 65(3):915–923

    Article  Google Scholar 

  • Deschênes JS, Wouwer AV (2016) Parameter identification of a dynamic model of cultures of microalgae scenedesmus obliquus-an experimental study. IFAC-PapersOnLine 49(7):1050–1055

  • Doma HS, El-Liethy MA, Abdo SM, Ali GH (2016) Potential of using high rate algal pond for algal biofuel production and wastewater treatment. Asian J Chem 28(2):399

    Article  CAS  Google Scholar 

  • Drewnowski J (2019) Advanced supervisory control system implemented at full-scale WWTP – A case study of optimization and energy balance improvement. Water 11(6):1218

    Article  CAS  Google Scholar 

  • Eroglu E, Smith SM, Raston CL (2015) Application of various immobilization techniques for algal bioprocesses. Biomass and Biofuels from Microalgae. Springer, Cham, pp 19–44

    Chapter  Google Scholar 

  • Eze VC, Velasquez-Orta SB, Hernández-García A, Monje-Ramírez I, Orta-Ledesma MT (2018) Kinetic modelling of microalgae cultivation for wastewater treatment and carbon dioxide sequestration. Algal Res 32:131–141

    Article  Google Scholar 

  • Fuentes J, Garbayo I, Cuaresma M, Montero Z, González-del-Valle M, Vílchez C (2016) Impact of microalgae-bacteria interactions on the production of algal biomass and associated compounds. Marine Drugs 14(5):100

    Article  Google Scholar 

  • Galès A, Bonnafous A, Carré C, Jauzein V, Lanouguère E, Le Floc’h E, Simier M (2019) Importance of ecological interactions during wastewater treatment using high rate algal ponds under different temperate climates. Algal Res 40:101508

    Article  Google Scholar 

  • Geetha S, Gouthami S (2016) Internet of things enabled real time water quality monitoring system. Smart Water 2(1):1

    Article  Google Scholar 

  • Giri BS, Gun S, Pandey S, Trivedi A, Kapoor RT, Singh RP, Sharma N (2020) Reusability of brilliant green dye contaminated wastewater using corncob biochar and Brevibacillus parabrevis: hybrid treatment and kinetic studies. Bioengineered 11(1):743–758

    Article  CAS  Google Scholar 

  • Gonçalves AL, Pires JC, Simões M (2017) A review on the use of microalgal consortia for wastewater treatment. Algal Res 24:403–415

    Article  Google Scholar 

  • Gour RS, Bairagi M, Garlapati VK, Kant A (2018) Enhanced microalgal lipid production with media engineering of potassium nitrate as a nitrogen source. Bioengineered 9(1):98–107

    Article  CAS  Google Scholar 

  • Granata F, Papirio S, Esposito G, Gargano R, De Marinis G (2017) Machine learning algorithms for the forecasting of wastewater quality indicators. Water 9(2):105

    Article  Google Scholar 

  • Gu W, Wang L, Liu Y, Liang P, Zhang X, Li Y, Huang X (2020) Anammox bacteria enrichment and denitrification in moving bed biofilm reactors packed with different buoyant carriers: Performances and mechanisms. Sci Total Environ 719:137277

    Article  CAS  Google Scholar 

  • Guo H, Jeong K, Lim J, Jo J, Kim YM, Park JP, Cho KH (2015) Prediction of effluent concentration in a wastewater treatment plant using machine learning models. J Environm Sci 32:90–101

    Article  CAS  Google Scholar 

  • Haimi H, Mulas M, Corona F, Vahala R (2013) Data-derived soft-sensors for biological wastewater treatment plants: An overview. Environm Modell Softw 47:88–107

    Article  Google Scholar 

  • Han HG, Liu HX, Liu Z, Qiao JF (2019) Fault detection of sludge bulking using a self-organizing type-2 fuzzy-neural-network. Control Eng Practice 90:27–37

    Article  Google Scholar 

  • Han HG, Zhang L, Liu HX, Qiao JF (2018) Multi objective design of fuzzy neural network controller for wastewater treatment process. Appl Soft Comput 67:467–478

    Article  Google Scholar 

  • Han H, Zhu S, Qiao J, Guo M (2018) Data-driven intelligent monitoring system for key variables in wastewater treatment process. Chin J Chem Eng 26(10):2093–2101

    Article  CAS  Google Scholar 

  • Havlik I, Lindner P, Scheper T, Reardon KF (2013) On-line monitoring of large cultivations of microalgae and cyanobacteria. Trends Biotechnol 31(7):406–414

    Article  CAS  Google Scholar 

  • Hena S, Znad H, Heong KT, Judd S (2018) Dairy farm wastewater treatment and lipid accumulation by Arthrospira platensis. Water Res 128:267–277

    Article  CAS  Google Scholar 

  • Hernández-del-Olmo F, Gaudioso E, Duro N, Dormido R (2019) Machine learning weather soft-sensor for advanced control of wastewater treatment plants. Sensors 19(14):3139

    Article  Google Scholar 

  • Huesemann M, Crowe B, Waller P, Chavis A, Hobbs S, Edmundson S, Wigmosta M (2016) A validated model to predict microalgae growth in outdoor pond cultures subjected to fluctuating light intensities and water temperatures. Algal Res 13:195–206

    Article  Google Scholar 

  • Iratni A, Chang NB (2019) Advances in control technologies for wastewater treatment processes: status, challenges, and perspectives. J Autom Sinica 6(2):337–363

    Article  Google Scholar 

  • Jia F, Kacira M, Ogden K (2015) Multi-wavelength based optical density sensor for autonomous monitoring of microalgae. Sensors 15(9):22234–22248

    Article  Google Scholar 

  • Kotsiantis SB, Kanellopoulos D, Pintelas PE (2006) Data preprocessing for supervised leaning. Int J Comput Sci 1(2):111–117

    Google Scholar 

  • Lee E, Jalalizadeh M, Zhang Q (2015) Growth kinetic models for microalgae cultivation: A review. Algal Res 12:497–512

    Article  Google Scholar 

  • Ling Y, Sun LP, Wang SY, Lin CSK, Sun Z, Zhou ZG (2019) Cultivation of oleaginous microalga Scenedesmus obliquus coupled with wastewater treatment for enhanced biomass and lipid production. Biochem Eng J 148:162–169

    Article  CAS  Google Scholar 

  • Liu J, Wu Y, Wu C, Muylaert K, Vyverman W, Yu HQ, Rittmann B (2017) Advanced nutrient removal from surface water by a consortium of attached microalgae and bacteria: A review. Biores Technol 241:1127–1137

    Article  CAS  Google Scholar 

  • Liu S, Zhang G, Zhang J, Li X, Li J (2016) Performance, carotenoids yield and microbial population dynamics in a photobioreactor system treating acidic wastewater: Effect of hydraulic retention time (HRT) and organic loading rate (OLR). Biores Technol 200:245–252

    Article  CAS  Google Scholar 

  • López ME, Rene ER, Boger Z, Veiga MC, Kennes C (2017) Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks. J Hazard Mater 324:100–109

    Article  Google Scholar 

  • Mahmoud AS, Ismail A, Mostafa MK, Mahmoud MS, Ali W, Shawky AM (2020) Isotherm and kinetic studies for heptachlor removal from aqueous solution using Fe/Cu nanoparticles, artificial intelligence, and regression analysis. Sep Sci Technol 55(4):684–696

    Article  CAS  Google Scholar 

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Modell Softw 15(1):101–124

    Article  Google Scholar 

  • Maier HR, Dandy GC (2001) Neural network based modelling of environmental variables: A systematic approach. Math Comput Modell 33(6):669–682

    Article  Google Scholar 

  • Mamandipoor B, Majd M, Sheikhalishahi S, Modena C, Osmani V (2020) Monitoring and detecting faults in wastewater treatment plants using deep learning. Environ Monit Assess 192(2):148

    Article  CAS  Google Scholar 

  • Mattson RR, Wildman M, Just C (2018) Submerged attached-growth reactors as lagoon retrofits for cold-weather ammonia removal: performance and sizing. Water Sci Technol 78(8):1625–1632

    Article  CAS  Google Scholar 

  • Manu DS, Thalla AK (2017) Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater. Appl Water Sci 7(7):3783–3791

    Article  CAS  Google Scholar 

  • Moreno-Garrido I (2008) Microalgae immobilization: current techniques and uses. Biores Technol 99(10):3949–3964

    Article  CAS  Google Scholar 

  • Mulas M, Corona F, Sirviö J, Hyvönen S, Vahala R (2016) Full-scale implementation of an advanced control system on a biological wastewater treatment plant. IFAC-Papers OnLine 49(7):1163–1168

    Article  Google Scholar 

  • Namin AH, Leboeuf K, Muscedere R, Wu H, Ahmadi M (2009). Efficient hardware implementation of the hyperbolic tangent sigmoid function. In 2009 IEEE International Symposium on Circuits and Systems, pp. 2117–2120. IEEE.

  • Nassef AM, Sayed ET, Rezk H, Abdelkareem MA, Rodriguez C, Olabi AG (2019) Fuzzy-modeling with particle swarm optimization for enhancing the production of biodiesel from microalga. Energy Sources, Part A: Recovery Utiliz Environ Effects 41(17):2094–2103

    Article  CAS  Google Scholar 

  • Naz M, Uyanik S, Yesilnacar MI, Sahinkaya E (2009) Side-by-side comparison of horizontal subsurface flow and free water surface flow constructed wetlands and artificial neural network (ANN) modelling approach. Ecol Eng 35(8):1255–1263

    Article  Google Scholar 

  • Newhart KB, Holloway RW, Hering AS, Cath TY (2019) Data-driven performance analyses of wastewater treatment plants: A review. Water Res 157:498–513

    Article  CAS  Google Scholar 

  • Niizawa I, Heinrich JM, Irazoqui HA (2014) Modeling of the influence of light quality on the growth of microalgae in a laboratory scale photo-bio-reactor irradiated by arrangements of blue and red LEDs. Biochem Eng J 90:214–223

    Article  CAS  Google Scholar 

  • Nwoba EG, Parlevliet DA, Laird DW, Alameh K, Moheimani NR (2019) Light management technologies for increasing algal photobioreactor efficiency. Algal Res 39:101433

    Article  Google Scholar 

  • Noguchi R, Ahamed T, Mikihide D, Watanabe MM (2018) A decision tree approach to estimate the microalgae production in open raceway pond. IOP Conf Ser: Earth Environ Sci 209(1):012050

    Google Scholar 

  • Noguchi R, Ahamed T, Rani DS, Sakurai K, Nasution MA, Wibawa DS, Demure M, Watanabe MM (2019) Artificial neural networks model for estimating growth of polyculture microalgae in an open raceway pond. Biosys Eng 177:122–129

    Article  Google Scholar 

  • Nõlvak H, Truu M, Oopkaup K, Kanger K, Krustok I, Nehrenheim E, Truu J (2018) Reduction of antibiotic resistome and integron-integrase genes in laboratory-scale photobioreactors treating municipal wastewater. Water Res 142:363–372

    Article  Google Scholar 

  • Olsson G, Carlsson B, Comas J, Copp J, Gernaey KV, Ingildsen P, Steyer JP (2014) Instrumentation, control and automation in wastewater-from London 1973 to Narbonne 2013. Water Sci Technol 69(7):1373–1385

    Article  CAS  Google Scholar 

  • Oswald WJ (1995) Ponds in the twenty-first century. Water Sci Technol 31(12):1–8

    Article  CAS  Google Scholar 

  • Oswald WJ, Gotaas HB, Golueke CG, Kellen WR, Gloyna EF, Hermann ER (1957) Algae in waste treatment. Sewage Indus Wastes 29(4):437–457

    Google Scholar 

  • Pang JW, Yang SS, He L, Chen YD, Cao GL, Zhao L, Ren NQ (2019) An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system. Chemosphere 234:893–901

    Article  CAS  Google Scholar 

  • Pittman JK, Dean AP, Osundeko O (2011) The potential of sustainable algal biofuel production using wastewater resources. Bioresour Technol 102(1):17–25

    Article  CAS  Google Scholar 

  • Posten C (2009) Design principles of photo-bioreactors for cultivation of microalgae. Eng Life Sci 9(3):165–177

    Article  CAS  Google Scholar 

  • Ramanan R, Kim BH, Cho DH, Oh HM, Kim HS (2016) Algae-bacteria interactions: evolution, ecology and emerging applications. Biotechnol Adv 34(1):14–29

    Article  CAS  Google Scholar 

  • Roscher R, Bohn B, Duarte MF, Garcke J (2019). Explainable machine learning for scientific insights and discoveries. arXiv:1905.08883.

  • Rene ER, Kim SJ, Lee DH, Je WB, López ME, Park HS (2012) Artificial neural network modelling of sequencing batch reactor performance. In: Handbook of Research on Computational Science and Engineering: Theory and Practice, pp. 456–479. IGI Global.

  • Samuelsson O, Björk A, Zambrano J, Carlsson B (2017) Gaussian process regression for monitoring and fault detection of wastewater treatment processes. Water Sci Technol 75(12):2952–2963

    Article  CAS  Google Scholar 

  • Sanchez-Fernández A, Fuente MJ, Sainz-Palmero GI (2015) Fault detection in wastewater treatment plants using distributed PCA methods. In 2015 IEEE 20th conference on emerging technologies & factory automation (ETFA), IEEE, pp 1–7

  • Sarker NK, Salam PA (2019) Indoor and outdoor cultivation of Chlorella vulgaris and its application in wastewater treatment in a tropical city - Bangkok. Thailand SN Appl Sci 1(12):1645

    Article  CAS  Google Scholar 

  • Sharma N, Solanki VK, Davim JP (2019) Basics of the internet of things (IoT) and its future. Handbook of IoT and Big Data, pp. 165–186.

  • Sibi P, Jones SA, Siddarth P (2013) Analysis of different activation functions using back propagation neural networks. J Theor Appl Inf Technol 47(3):1264–1268

    Google Scholar 

  • Singh V, Pandey ND, Singh RP (2016) Evaluation of dynamic performance in terms of effluent OD and biomass concentrations of UASB reactor treating low strength wastewater. Int J Appl Sci-Res Rev 3(1):50–63

    Google Scholar 

  • Sun Z, Li T, Zhou ZG, Jiang Y (2015) Microalgae as a source of lutein: chemistry, biosynthesis, and carotenogenesis. In: Microalgae Biotechnology (pp. 37–58). Springer, Cham

  • Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 847–855.

  • Thürlimann CM, Dürrenmatt DJ, Villez K (2018) Soft-sensing with qualitative trend analysis for wastewater treatment plant control. Control Eng Practice 70:121–133

    Article  Google Scholar 

  • Thürlimann CM, Udert KM, Morgenroth E, Villez K (2019) Stabilizing control of a urine nitrification process in the presence of sensor drift. Water Res 165:114958

    Article  Google Scholar 

  • Tran DT, Van Do TC, Nguyen QT, Le TG (2020) Simultaneous removal of pollutants and high value biomaterials production by Chlorella variabilis TH03 from domestic wastewater. Clean Technologies and Environmental Policy, pp. 1–15.

  • Treloar NJ, Fedorec AJ, Barnes CP (2018) Deep reinforcement learning for the control of bacterial populations in bioreactors. bioRxiv, 457366. https://doi.org/10.1101/457366

  • Torregrossa D, Leopold U, Hernández-Sancho F, Hansen J (2018) Machine learning for energy cost modelling in wastewater treatment plants. J Environ Manage 223:1061–1067

    Article  Google Scholar 

  • Tuggener L, Amirian M, Rombach K, Lörwald S, Varlet A, Westermann C, Stadelmann T (2019) Automated machine learning in practice: state of the art and recent results. In: IEEE 6th Swiss Conference on Data Science (SDS), pp. 31–36.

  • Ugwu CU, Aoyagi H, Uchiyama H (2008) Photobioreactors for mass cultivation of algae. Bioresour Technol 99(10):4021–4028

    Article  CAS  Google Scholar 

  • Umamaheswari J, Shanthakumar S (2019) Phycoremediation of paddy-soaked wastewater by indigenous microalgae in open and closed culture system. J Environ Manage 243:435–443

    Article  CAS  Google Scholar 

  • Vuppaladadiyam AK, Prinsen P, Raheem A, Luque R, Zhao M (2018) Microalgae cultivation and metabolites production: a comprehensive review. Biofuels, Bioprod Biorefin 12(2):304–324

    Article  CAS  Google Scholar 

  • Wang B, Li Z, Dai Z, Lawrence N, Yan X (2019) A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant. Appl Soft Comput 82:105527

    Article  Google Scholar 

  • Whitton R, Ometto F, Pidou M, Jarvis P, Villa R, Jefferson B (2015) Microalgae for municipal wastewater nutrient remediation: mechanisms, reactors and outlook for tertiary treatment. Environ Technol Rev 4(1):133–148

    Article  CAS  Google Scholar 

  • Xiao H, Huang D, Pan Y, Liu Y, Song K (2017) Fault diagnosis and prognosis of wastewater processes with incomplete data by the auto-associative neural networks and ARMA model. Chem Intell Lab Syst 161:96–107

    Article  CAS  Google Scholar 

  • Yen HW, Hu IC, Chen CY, Nagarajan D, Chang JS (2019) Design of photobioreactors for algal cultivation. Biofuels from Algae, pp. 225–256. Elsevier.

  • Yetilmezsoy K (2018) Applications of soft computing methods in environmental engineering. Handbook Environ Mater Manag 11:1–47

    Google Scholar 

  • Yoo CK, Villez K, Lee IB, Rosén C, Vanrolleghem PA (2007) Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor. Biotechnol Bioeng 96(4):687–701

    Article  CAS  Google Scholar 

  • Yuan Z, Olsson G, Cardell-Oliver R, van Schagen K, Marchi A, Deletic A, Jiang G (2019) Sweating the assets-the role of instrumentation, control and automation in urban water systems. Water Res 155:381–402

    Article  CAS  Google Scholar 

  • Zarkami R, Hesami H, Pasvisheh RS (2020) Assessment, monitoring and modelling of the abundance of Dunaliella salina Teod in the Meighan wetland, Iran using decision tree model. Environ Monit Assess 192(3):172

    Article  CAS  Google Scholar 

  • Zhang GY, Guo SY, Li L, Zhou WB, Cai MY (2001) Neural networks for modelling and predicting the Chlorella Protothecoides cultivation processes. Algae and their Biotechnological Potential. Springer, Dordrecht, pp 79–92

    Chapter  Google Scholar 

  • Zhang Q, Hong Y (2014) Effects of stationary phase elongation and initial nitrogen and phosphorus concentrations on the growth and lipid-producing potential of Chlorella sp HQ. J Appl Phycol 26(1):141–149

    Article  CAS  Google Scholar 

  • Zhang Y, Wang J, Tan L (2017) Uptake and transformation of carbon, nitrogen and phosphorus in the co-culture system of algae and bacteria in seawater. Acta Ecol Sin 2017(14):25

    Google Scholar 

  • Zhao LJ, Chai TY, Yuan DC (2012) Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants. Int J Autom Comput 9(6):627–633

    Article  Google Scholar 

  • Zhao X, Ding S, An Y, Jia W (2018) Asynchronous reinforcement learning algorithms for solving discrete space path planning problems. Applied Intelligence 48(12):4889–4904

    Article  Google Scholar 

  • Zhu L, Wang Z, Shu Q, Takala J, Hiltunen E, Feng P, Yuan Z (2013) Nutrient removal and biodiesel production by integration of freshwater algae cultivation with piggery wastewater treatment. Water Res 47(13):4294–4302

    Article  CAS  Google Scholar 

  • Zhu S, Qin L, Feng P, Shang C, Wang Z, Yuan Z (2019) Treatment of low C/N ratio wastewater and biomass production using co-culture of Chlorella vulgaris and activated sludge in a batch photobioreactor. Biores Technol 274:313–320

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank Asian Institute of Technology, Thailand, for providing an opportunity to collaborate and work on this review paper. O.A. Ramírez Calderón, O.M. Abdeldayem and J. Lazaro Gil thank the Erasmus+ International Master of Science in Environmental Technology and Engineering (IMETE) for supporting their M.Sc study at UCT Prague (Czech Republic), IHE Delft (The Netherlands), and Ghent University (Belgium). E.R. Rene thanks IHE Delft for providing staff time support under the project “Support to Society” to co-work with researchers from Taiwan, Mongolia and students of the ERASMUS+ IMETE programme.

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Authors and Affiliations

  1. Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, 10617, Taiwan, People’s Republic of China

    Batsuren Sundui

  2. Department of Environmental Engineering and Water Technology, IHE Delft Institute for Water Education, PO Box 3015, 2601 DA, Delft, The Netherlands

    Olga Alejandra Ramirez Calderon, Omar M. Abdeldayem, Jimena Lázaro-Gil & Eldon R. Rene

  3. Department of Information and Computer Science, School of Engineering and Applied Sciences, National University of Mongolia, Building #3, Baga toiruu-2, P.O. Box 46A/600, Sukhbaatar, Ulaanbaatar, 14200, Mongolia

    Uyanga Sambuu

Authors
  1. Batsuren Sundui
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  2. Olga Alejandra Ramirez Calderon
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  3. Omar M. Abdeldayem
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  4. Jimena Lázaro-Gil
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  5. Eldon R. Rene
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  6. Uyanga Sambuu
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Correspondence to Batsuren Sundui or Eldon R. Rene.

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Sundui, B., Ramirez Calderon, O.A., Abdeldayem, O.M. et al. Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives. Clean Techn Environ Policy 23, 127–143 (2021). https://doi.org/10.1007/s10098-020-01993-x

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