The assessment of response surface methodology (RSM) and artificial neural network (ANN) modeling in dry flue gas desulfurization at low temperatures

References

• Ritchie, H.; Roser, M. Energy. Our World in Data. 2020.
   Google Scholar
• Denchak, M. Fossil Fuels: The Dirty Facts. 2018.
   Google Scholar
• Lazkano, I.; Nøstbakken, L.; Pelli, M. From Fossil Fuels to Renewables: The Role of Electricity Storage. Eur. Econ. Rev. 2017, 99, 113–129. DOI: 10.1016/j.euroecorev.2017.03.013.
   Web of Science ®Google Scholar
• Guigard, D. S. Sulphur Dioxide: Environmental Effects, Fate and Behaviour. Alberta Environment, Science & Standards Branch: Edmonton, Canada, 2003.
   Google Scholar
• Guttikunda, S. K.; Jawahar, P. Atmospheric Emissions and Pollution from the Coal-Fired Thermal Power Plants in India. Atmos. Environ. 2014, 92, 449–460. DOI: 10.1016/j.atmosenv.2014.04.057.
   Web of Science ®Google Scholar
• Munawer, M. E. Human Health and Environmental Impacts of Coal Combustion and Post-Combustion Wastes. J. Sustainable Min. 2018, 17, 87–96. DOI: 10.1016/j.jsm.2017.12.007.
   Google Scholar
• Calvo, V. L. V.; Giner-Santonja, G.; Alonso-Fariñas, B.; Aguado, J. M. The Effect of the European Industrial Emissions Directive on the Air Emission Limit Values Set by Competent Authorities in the Permitting Procedure: The Case of the Spanish Cement Industry. Sci Total Environ 2021, 773, 145491. DOI: 10.1016/j.scitotenv.2021.145491.
   PubMed Web of Science ®Google Scholar
• Davidson, J.; Norbeck, J. M. An Interactive History of the Clean Air Act: Scientific and Policy Perspectives. Elsevier: Amsterdam, Netherlands, 2011.
   Google Scholar
• Ladwig, K. J.; Blythe, G. M. Flue-Gas Desulfurization Products and Other Air Emissions Controls. In Coal Combustion Products (CCP’s); Robl, T., Oberlink. A., Jones, R., Eds., Elsevier: Amsterdam, Netherlands, 2017; pp 67–95.
   Google Scholar
• Li, X.; Han, J.; Liu, Y.; Dou, Z.; Zhang, T. Summary of Research Progress on Industrial Flue Gas Desulfurization Technology. Sep. Purif. Technol. 2022, 281, 119849. DOI: 10.1016/j.seppur.2021.119849.
   Web of Science ®Google Scholar
• Carpenter, A. M. Low Water FGD Technologies. 2012, p 73.
   Google Scholar
• Srivastava, R. K.; Jozewicz, W. Flue Gas Desulfurization: The State of the Art. J. Air Waste Manag. Assoc. 2001, 51, 1676–1688. DOI: 10.1080/10473289.2001.10464387.
   PubMedGoogle Scholar
• Ogenga, D. O.; Siagi, Z. O.; Onyango, M. S.; Mbarawa, M. An Overview the Use of Ca (OH) 2/Fly Ash in Flue Gas Desulphurization. R&D J. South African Inst. Mech. Eng. 2008, 24, 4–8.
   Google Scholar
• Rathnayake, M.; Julnipitawong, P.; Tangtermsirikul, S.; Toochinda, P. Utilization of Coal Fly Ash and Bottom Ash as Solid Sorbents for Sulfur Dioxide Reduction from Coal Fired Power Plant: Life Cycle Assessment and Applications. J. Cleaner Prod. 2018, 202, 934–945. DOI: 10.1016/j.jclepro.2018.08.204.
   Web of Science ®Google Scholar
• Rikker, Y. O.; Kobylkin, M. V.; Batukhtin, A. G. Improving the Efficiency of Flue Gas Desulphurization of TPS. In IOP Conference Series: Materials Science and Engineering; IOP Publishing, 2019.
   Google Scholar
• Kumar, L.; Jana, S. K. Advances in Absorbents and Techniques Used in Wet and Dry FGD: A Critical Review. Rev. Chem. Eng. 2021, 38, 843–880. DOI: 10.1515/revce-2020-0029.
   Web of Science ®Google Scholar
• Poullikkas, A. Review of Design, Operating, and Financial Considerations in Flue Gas Desulfurization Systems. Energy Technol. Policy 2015, 2, 92–103. DOI: 10.1080/23317000.2015.1064794.
   Google Scholar
• Ren, Y.; Bao, H. Modeling and Simulation of Metallurgical Process Based on Hybrid Petri Net. In IOP Conference Series: Materials Science and Engineering; IOP Publishing, 2016.
   Google Scholar
• Lang, S.; Reggelin, T.; Müller, M.; Nahhas, A. Open-Source Discrete-Event Simulation Software for Applications in Production and Logistics: An Alternative to Commercial Tools? Proc. Comput. Sci. 2021, 180, 978–987. DOI: 10.1016/j.procs.2021.01.349.
   Google Scholar
• Chemmangattuvalappil, N. G.; Chon, C. H.; Sum, D. N. K.; Elyas, R.; Chen, C.-L.; Lee, H.-Y.; Elms, R. D. Chemical Engineering Process Simulation. Elsevier: Amsterdam, Netherlands, 2017.
   Google Scholar
• Chaves, I. D. G.; López, J. R. G.; Zapata, J. L. G.; Robayo, A. L.; Niño, G. R. Process Simulation in Chemical Engineering. In Process Analysis and Simulation in Chemical Engineering; Chaves, I. D. G.; López, J. R. G.; Zapata, J. L. G.; Robayo, A. L.; Niño, G. R., Eds., Springer: Switzerland, 2016; pp 1–51.
   Google Scholar
• Barone, G.; Buonomano, A.; Forzano, C.; Palombo, A. A Novel Dynamic Simulation Model for the Thermo-Economic Analysis and Optimisation of District Heating Systems. Energy Convers. Manage. 2020, 220, 113052. DOI: 10.1016/j.enconman.2020.113052.
   Web of Science ®Google Scholar
• Tryggvason, G. Computational Fluid Dynamics. Evolution 2017, 1, 2.
   Google Scholar
• Maina, P.; Mbarawa, M. Enhancement of Lime Reactivity by Addition of Diatomite. Fuel Process. Technol. 2011, 92, 1910–1919. DOI: 10.1016/j.fuproc.2011.05.011.
   Web of Science ®Google Scholar
• Amjed, N.; Iqbal, M.; Bhatti, I. A.; Nazir, A. Coal Desulphurization and Conditions Optimization through Response Surface Methodology, Khushab Mines, Pakistan. Energy Sources Part A 2017, 39, 1235–1241. DOI: 10.1080/15567036.2017.1319439.
   Google Scholar
• Kumar, L.; Jana, S. K.; Datta, D. Application of Response Surface Methodology to Absorptive Separation of SO2 from Its Mixture with Air Using Marble Waste. Chem. Eng. Commun. 2020, 207, 458–473. DOI: 10.1080/00986445.2019.1605358.
   Web of Science ®Google Scholar
• El Bakkari, M.; Bindiganavile, V.; Boluk, Y. Facile Synthesis of Calcium Hydroxide Nanoparticles onto TEMPO-Oxidized Cellulose Nanofibers for Heritage Conservation. ACS Omega 2019, 4, 20606–20611. DOI: 10.1021/acsomega.9b02643.
   PubMed Web of Science ®Google Scholar
• Karatepe, N.; Erdoğan, N.; Ersoy-Meriçboyu, A.; Küçükbayrak, S. Preparation of Diatomite/Ca(OH)2 Sorbents and Modelling Their Sulphation Reaction. Chem. Eng. Sci. 2004, 59, 3883–3889. DOI: 10.1016/j.ces.2004.04.013.
   Web of Science ®Google Scholar
• Aydar, A. Y. Utilization of Response Surface Methodology in Optimization of Extraction of Plant Materials. In Statistical Approaches with Emphasis on Design of Experiments Applied to Chemical Processes; Silva, V., Ed., IntechOpen: London, UK, 2018; pp 157–169.
   Google Scholar
• Patel, K. A.; Brahmbhatt, P. K. Response Surface Methodology Based Desirability Approach for Optimization of Roller Burnishing Process Parameter. J. Inst. Eng. India Ser. C 2018, 99, 729–736. DOI: 10.1007/s40032-017-0368-8.
   Google Scholar
• Kumari, M.; Gupta, S. K. Response Surface Methodological (RSM) Approach for Optimizing the Removal of Trihalomethanes (THMs) and Its Precursor’s by Surfactant Modified Magnetic Nanoadsorbents (sMNP)-an Endeavor to Diminish Probable Cancer Risk. Sci. Rep. 2019, 9, 1–11. DOI: 10.1038/s41598-019-54902-8.
   PubMed Web of Science ®Google Scholar
• Mumtaz, M. W.; Adnan, A.; Mukhtar, H.; Rashid, U.; Danish, M. Biodiesel Production through Chemical and Biochemical Transesterification. In Clean Energy for Sustainable Development; Rasul, M. G., Azad, A. K., Sharma, S. C., Eds., Elsevier: Amsterdam, Netherlands, 2017; pp 465–485.
   Google Scholar
• Breig, S. J. M.; Luti, K. J. K. Response Surface Methodology: A Review on Its Applications and Challenges in Microbial Cultures. Mater. Today: Proc. 2021, 42, 2277–2284.
   Google Scholar
• Rahman, A. S. Effects of Nanofibers on Properties of Geopolymer Composites. In Nanotechnology in Eco-Efficient Construction; Jones, G., Ed., Elsevier: Amsterdam, Netherlands, 2019; pp 123–140.
   Google Scholar
• Sarabia, L. A.; Ortiz, M. C.; Sánchez, M. S. Response Surface Methodology. 2020.
   Google Scholar
• Bohani, F. A.; Suliman, A.; Saripuddin, M.; Sameon, S. S.; Md Salleh, N. S.; Nazeri, S. A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection. J. Electr. Comput. Eng. 2021, 2021, e9136206–10. DOI: 10.1155/2021/9136206.
   Web of Science ®Google Scholar
• Opěla, P.; Schindler, I.; Kawulok, P.; Kawulok, R.; Rusz, S.; Sauer, M. Shallow and Deep Learning of an Artificial Neural Network Model Describing a Hot Flow Stress Evolution: A Comparative Study. Mater. Des. 2022, 220, 110880. DOI: 10.1016/j.matdes.2022.110880.
   Web of Science ®Google Scholar
• Tao, Q.; Li, L.; Huang, X.; Xi, X.; Wang, S.; Suykens, J. A. Piecewise linear Neural Networks and Deep Learning. Nat. Rev. Methods Primers 2022, 2, 1–17. DOI: 10.1038/s43586-022-00125-7.
   Google Scholar
• Xu, Y.; Zhou, Y.; Sekula, P.; Ding, L. Machine Learning in Construction: From Shallow to Deep Learning. Dev. Built Environ. 2021, 6, 100045. DOI: 10.1016/j.dibe.2021.100045.
   Google Scholar
• Eesa, A. S.; Arabo, W. K. A Normalization Methods for Backpropagation: A Comparative Study. Sci. J. Univ. Zakho 2017, 5, 319–323. DOI: 10.25271/2017.5.4.381.
   Google Scholar
• Park, Y.-S.; Lek, S. Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling. In Developments in Environmental Modelling; Jørgensen, S. E., Ed., Elsevier: Amsterdam, Netherlands, 2016; Vol. 28; pp 123–140.
   Google Scholar
• Asaad, R. R.; Ali, R. I. Back Propagation Neural Network (BPNN) and Sigmoid Activation Function in Multi-Layer Networks. Acad. J. Nawroz Univ. 2019, 8, 216–221. DOI: 10.25007/ajnu.v8n4a464.
   Google Scholar
• Dubey, S. R.; Singh, S. K.; Chaudhuri, B. B. Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark. Neurocomputing 2022, 503, 92–108. DOI: 10.1016/j.neucom.2022.06.111.
   Web of Science ®Google Scholar
• Dubey, S. R.; Chakraborty, S. Average Biased ReLU Based CNN Descriptor for Improved Face Retrieval. Multimed. Tools Appl. 2021, 80, 23181–23206. DOI: 10.1007/s11042-020-10269-x.
   Web of Science ®Google Scholar
• Sharma, S.; Sharma, S.; Athaiya, A. Activation Functions in Neural Networks. IJEAST 2020, 04, 310–316. DOI: 10.33564/IJEAST.2020.v04i12.054.
   Google Scholar
• Kabuba, J.; Banza, M. Ion-Exchange Process for the Removal of Ni (II) and Co (II) from Wastewater Using Modified Clinoptilolite: modeling by Response Surface Methodology and Artificial Neural Network. Results Eng. ineering 2020, 8, 100189. DOI: 10.1016/j.rineng.2020.100189.
   Google Scholar
• Maran, J. P.; Sivakumar, V.; Thirugnanasambandham, K.; Sridhar, R. Artificial Neural Network and Response Surface Methodology Modeling in Mass Transfer Parameters Predictions during Osmotic Dehydration of Carica Papaya L. Alexandria Eng. J. 2013, 52, 507–516. DOI: 10.1016/j.aej.2013.06.007.
   Google Scholar
• Thoai, D. N.; Tongurai, C.; Prasertsit, K.; Kumar, A. Predictive Capability Evaluation of RSM and ANN in Modeling and Optimization of Biodiesel Production from Palm (Elaeisguineensis) Oil. Int. J. Appl. Eng. Res. 2018, 13, 7529–7540.
   Google Scholar
• Jha, A. K.; Sit, N. Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Modelling for Supercritical Fluid Extraction of Phytochemicals from Terminalia Chebula Pulp and Optimization Using RSM Coupled with Desirability Function (DF) and Genetic Algorithm (GA) and ANN with GA. Ind. Crops Prod. 2021, 170, 113769. DOI: 10.1016/j.indcrop.2021.113769.
   Web of Science ®Google Scholar
• Sagbas, A.; Gürtuna, F.; Polat, U. Comparison of ANN and RSM Modeling Approaches for WEDM Process Optimization. Mater. Test. 2021, 63, 386–392. DOI: 10.1515/mt-2020-0057.
   Web of Science ®Google Scholar
• Patel, K. A.; Brahmbhatt, P. K. A Comparative Study of the RSM and ANN Models for Predicting Surface Roughness in Roller Burnishing. Proc. Technol. 2016, 23, 391–397. DOI: 10.1016/j.protcy.2016.03.042.
   Google Scholar
• Vu, N.-T.; Do, K.-U. Prediction of Ammonium Removal by Biochar Produced from Agricultural Wastes Using Artificial Neural Networks: Prospects and Bottlenecks. In Soft Computing Techniques in Solid Waste and Wastewater Management; Karri, R. R., Ravindran, G., Dehghani, M. H., Eds., Elsevier: Amsterdam, Netherlads, 2021; pp 455–467.
   Google Scholar
• Zayani, R.; Bouallegue, R.; Roviras, D. Levenberg-Marquardt Learning Neural Network for Adaptive Predistortion for Time-Varying HPA with Memory in OFDM Systems. In 2008 16th European Signal Processing Conference; IEEE: 2008.
   Google Scholar
• Danmaliki, G. I.; Saleh, T. A.; Shamsuddeen, A. A. Response surface Methodology Optimization of Adsorptive Desulfurization on Nickel/Activated Carbon. Chem. Eng. J. 2017, 313, 993–1003. DOI: 10.1016/j.cej.2016.10.141.
   Web of Science ®Google Scholar
• Dahlan, I.; Lee, K. T.; Kamaruddin, A. H.; Mohamed, A. R. Analysis of SO2 Sorption Capacity of Rice Husk Ash (RHA)/CaO/NaOH Sorbents Using Response Surface Methodology (RSM): Untreated and Pretreated RHA. Environ. Sci. Technol. 2008, 42, 1499–1504. DOI: 10.1021/es702232k.
   PubMed Web of Science ®Google Scholar
• Germec, M.; Turhan, I. Enhanced Production of Aspergillus Niger Inulinase from Sugar Beet Molasses and Its Kinetic Modeling. Biotechnol. Lett. 2020, 42, 1939–1955. DOI: 10.1007/s10529-020-02913-1.
   PubMed Web of Science ®Google Scholar
• Atalar, I.; Dervisoglu, M. Optimization of Spray Drying Process Parameters for Kefir Powder Using Response Surface Methodology. LWT - Food Sci. Technol. 2015, 60, 751–757. DOI: 10.1016/j.lwt.2014.10.023.
   Web of Science ®Google Scholar
• Pires, F. C. S.; Silva Pena, R. d Optimization of Spray Drying Process Parameters for Tucupi Powder Using the Response Surface Methodology. J. Food Sci. Technol. 2017, 54, 3459–3472. DOI: 10.1007/s13197-017-2803-5.
   PubMed Web of Science ®Google Scholar
• Bejani, M. M.; Ghatee, M. A Systematic Review on Overfitting Control in Shallow and Deep Neural Networks. Artif. Intell. Rev. 2021, 54, 6391–6438. DOI: 10.1007/s10462-021-09975-1.
   Web of Science ®Google Scholar
• Alhnaity, B.; Pearson, S.; Leontidis, G.; Kollias, S. Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments. In International Symposium on Advanced Technologies and Management for Innovative Greenhouses: GreenSys2019 1296, 2019.
   Google Scholar
• Fatiha, B.; Sameh, B.; Youcef, S.; Zeineddine, D.; Nacer, R. Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in Optimization of the Immobilization Conditions for Lipase from Candida Rugosa on Amberjet® 4200-Cl. Prep. Biochem. Biotechnol. 2013, 43, 33–47. DOI: 10.1080/10826068.2012.693899.
   PubMed Web of Science ®Google Scholar
• Youssefi, S.; Emam-Djomeh, Z.; Mousavi, S. M. Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice. Drying Technol. 2009, 27, 910–917. DOI: 10.1080/07373930902988247.
   Web of Science ®Google Scholar