Pollutant monitoring in tail gas of sulfur recovery unit with statistical and soft computing models

References

• Awan, M.S.K. and Awais, M.M. (2011). Predicting weather events using fuzzy rule based system. Appl. Soft. Comput., 11, 56–63.
   Google Scholar
• Banu, U. S., and Uma, G. (2011). ANFIS based sensor fault detection for continuous stirred tank reactor. Appl. Soft. Comput., 11(2), 2618–2624.
   Web of Science ®Google Scholar
• Casali, A., Gonzalez, G., Torres, F., Vallebuona, G., Castelli, L., and Gimenez, P. (1998). Particle size distribution soft-sensor for a grinding circuit. Powder. Technol., 99, 15–21.
   Google Scholar
• Cheng, Z. and Liu, X. (2015). Optimal online soft sensor for product quality monitoring in propylene polymerization process. Neurocomputing, 149, 1216–1224.
   Web of Science ®Google Scholar
• De Jong, S. (1993). SIMPLS: an alternative approach to partial least squares regression. Chemom. Intell. Lab. Syst., 18, 251–263.
   Google Scholar
• Di Bella, A., Fortuna, L., Graziani, S., Napoli, G., and Xibilia, M. G. (2007). Soft Sensor design for a Sulphur Recovery Unit using Genetic Algorithms. Paper presented at IEEE International Symposium on Intelligent Signal Processing, 2007. WISP 2007, Alcala de Henares, Spain, pp. 1–6.
   Google Scholar
• Di Sciascio, F., and Amicarelli, A. N. (2008). Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression. Comput. Chem. Eng., 32, 3264–3273.
   Google Scholar
• Dufour, P., Bhartiya, S., Dhurjati, P.S., and Doyle, F.J. (2005). Neural network-based software sensor: Training set design and application to a continuous pulp digester. Control. Eng. Pract., 13, 135–143.
   Web of Science ®Google Scholar
• Fortuna, L., Graziani, S., Rizzo, A., and Xibilia, M. G. (2007). Soft Sensors for Monitoring and Control of Industrial Processes, 1st ed., Springer-Verlag, London.
   Google Scholar
• Fortuna, L., Graziani, S., Napoli, G., and Xibilia, M. G. (2006). Stacking approaches for the design of a soft sensor for a Sulphur Recovery Unit. Paper presented at IECON 2006-32nd Annual Conference on IEEE Industrial Electronics, November 6–10, in Paris, France, pp. 229–234.
   Google Scholar
• Fortuna, L., Rizzo, A., Sinatra, M., and Xibilia, M. G. (2003). Soft analyzers for a sulphur recovery unit. Control. Eng. Pract., 11, 1491–1500.
   Web of Science ®Google Scholar
• Ge, Z., Chen, T., and Song, Z. (2011). Quality prediction for polypropylene production process based on CLGPR model, Control. Eng. Pract., 19, 423–432.
   Web of Science ®Google Scholar
• Ge, Z., and Song, Z. (2010). Nonlinear soft sensor development based on relevance vector machine. Ind. Eng. Chem. Res., 49, 8685–8693.
   Google Scholar
• Ge, Z., and Song, Z. (2011). Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples. AIChE J., 57, 2109–2119.
   Web of Science ®Google Scholar
• Geladi, P., and Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Anal. Chim. Acta., 185, 1–17.
   Web of Science ®Google Scholar
• Gibbs, M. N. (1998). Bayesian Gaussian processes for regression and classification (Doctoral dissertation, University of Cambridge).
   Google Scholar
• Graziani, S., and Xibilia, M. G. (2017). A deep learning based soft sensor for a sour water stripping plant. Paper presented at 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May, in Turin, Italy, pp. 1–6.
   Google Scholar
• Hoerl, A. E., and Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55–67.
   Web of Science ®Google Scholar
• Jahedsaravani, A., Massinaei, M., and Marhaban, M. H. (2016). Application of image processing and adaptive neuro-fuzzy system for estimation of the metallurgical parameters of a flotation process. Chem. Eng. Commun., 203, 1395–1402.
   Web of Science ®Google Scholar
• Jain, V., Kishore, P., Kumar, R. A., Pani, A. K. (2017). Inferential sensing of output quality in petroleum refinery using principal component regression and support vector regression. Paper presented at 2017 IEEE 7th International Advance Computing Conference (IACC), January, in Hyderabad, India, pp. 461–465.
   Google Scholar
• Jang, J.-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE. Transac. Syst. Man Cybernet., 23, 665–685.
   Web of Science ®Google Scholar
• Kadlec, P., Gabrys, B., and Strandt, S. (2009). Data-driven soft sensors in the process industry. Comput. Chem. Eng., 33, 795–814.
   Google Scholar
• Kennard, R. W., and Stone, L. A. (1969). Computer aided design of experiments. Technometrics, 11, 137–148.
   Web of Science ®Google Scholar
• Khalaf, G., and Shukur, G. (2005). Choosing ridge parameters for regression problems. Commun. Stat.Theory Methods., A34, 1177–1182.
   Web of Science ®Google Scholar
• Kibria, B. M. G. (2003). Performance of some new ridge regression estimators. Commun. Stat.Theory Methods., 32, 419–435.
   Web of Science ®Google Scholar
• Kourti, T., and MacGregor, J. F. (1995). Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemom. Intell. Lab. Syst., 28, 3–21.
   Google Scholar
• Lv, Z., Liu, Y., Zhao, J., and Wang, W. (2015). Soft computing for overflow particle size in grinding process based on hybrid case based reasoning. Appl. Soft. Comput., 27, 533–542.
   Web of Science ®Google Scholar
• MacKay, D. J. (1992). Bayesian interpolation. Neural. Comput., 4, 415–447.
   Web of Science ®Google Scholar
• Mardia, K. V., and Marshall, R. J. (1984). Maximum likelihood estimation of models for residual covariance in spatial regression. Biometrika, 71, 135–146.
   Web of Science ®Google Scholar
• Mareš, J., Kukal, J., Hrnčiřík, P., and Náhlík, J. (2016). Process state classification of fed-batch fermentation based on process variables analysis. Biochem. Eng. J., 112, 178–185.
   Google Scholar
• Marquardt, D. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12, 591–612.
   Web of Science ®Google Scholar
• Neal, R. M. (1997). Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Tech. rep., Dept. of Statistics, University of Toronto.
   Google Scholar
• Novak, M., Mohler, I., Golob, M., Andrijić, Ž. U., and Bolf, N. (2013). Continuous estimation of kerosene cold filter plugging point using soft sensors. Fuel. Sci. Technol., 113, 8–19.
   Web of Science ®Google Scholar
• Pani, A. K., and Mohanta, H. K. (2015). Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network. ISA transactions, 56, 206–221.
   PubMed Web of Science ®Google Scholar
• Pani, A.K., Vadlamudi, V.K., and Mohanta, H.K. (2013). Development and comparison of neural network based soft sensors for online estimation of cement clinker quality. ISA transactions, 52, 19–29.
   PubMed Web of Science ®Google Scholar
• Pani, A. K., and Mohanta, H. K. (2011). A survey of data treatment techniques for soft sensor design. Chem. Prod. Process. Model., 6(1), ISSN (Online) 1934-2659.
   Google Scholar
• Pani, A. K., and Mohanta, H. K. (2016). Online monitoring of cement clinker quality using multivariate statistics and Takagi-Sugeno fuzzy-inference technique. Control. Eng. Pract., 57, 1–17.
   Web of Science ®Google Scholar
• Pasolli, L., Melgani, F., and Blanzieri, E. (2010). Gaussian process regression for estimating chlorophyll concentration in subsurface waters from remote sensing data. Geosci. Remote Sens. Lett., 7, 464–468.
   Web of Science ®Google Scholar
• Rasmussen, C. E. (1997). Evaluation of Gaussian processes and other methods for non-linear regression (Doctoral dissertation, National Library of Canada).
   Google Scholar
• Rasmussen, C. E., and Nickisch, H. (2010). Gaussian processes for machine learning (GPML) toolbox. J. Mach. Learn. Res., 11, 3011–3015.
   Web of Science ®Google Scholar
• Salah B., Zoheir M., Slimane Z., and Jurgen B. (2015). Inferential sensor-based adaptive principal components analysis of mould bath level for breakout defect detection and evaluation in continuous casting. Appl. Soft. Comput., 34, 120–128.
   Web of Science ®Google Scholar
• Sarimveis, H. (2001). Inferential sensors for on-line monitoring of tissue machine quality properties. Chem. Eng. Commun., 188, 231–242.
   Web of Science ®Google Scholar
• Savic, M., Mihajlovic, I., Djordjevic, P., and Zivkovic, Z. (2016). ANFIS-based prediction of the decomposition of sodium aluminate solutions in the bayer process. Chem. Eng. Commun., 203, 1053–1061.
   Web of Science ®Google Scholar
• Shang C., Yang F., Huang D., Lyu W. (2014). Data-driven soft sensor development based on deep learning technique. J. Process. Contr., 24, 223–233.
   Google Scholar
• Shang, C., Huang, B., Yang, F., and Huang, D. (2015). Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling. AIChE Journal, 61, 4126–4139.
   Web of Science ®Google Scholar
• Shao, W., and Tian, X. (2015). Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models. Chem. Eng. Res. Des., 95, 113–132.
   Web of Science ®Google Scholar
• Shao, W., and Tian, X. (2017). Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development. Neurocomputing, 222, 91–104.
   Web of Science ®Google Scholar
• Shao, W., Tian, X., and Wang, P. (2015). Soft sensor development for nonlinear and time‐varying processes based on supervised ensemble learning with improved process state partition. Asia-Pac. J. Chem. Eng., 10, 282–296.
   Web of Science ®Google Scholar
• Shao, W., Tian, X., and Wang, P. (2015). Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor. Chin. J. Chem. Eng., 23, 1925–1934.
   Web of Science ®Google Scholar
• Shokri, S., Marvast, M. A., Sadeghi, M. T., and Narasimhan, S. (2016). Combination of data rectification techniques and soft sensor model for robust prediction of sulphur content in HDS process. J. Taiwan. Inst. Chem. Eng, 58, 117–126.
   Web of Science ®Google Scholar
• Su, Z., Wang, P., Shen, J., Yu, X., Lv, Z., and Lu, L. (2011). Multi-model strategy based evidential soft sensor model for predicting evaluation of variables with uncertainty. Appl. Soft. Comput., 11, 2595–2610.
   Web of Science ®Google Scholar
• Tang, Q., Lau, Y, B., Hu, S., Yan, W., Yang, Y., and Chen, T. (2010). Response surface methodology using Gaussian processes: Towards optimizing the trans-stilbene epoxidation over Co2+–NaX catalysts, Chem. Eng. J., 156, 423–431.
   Web of Science ®Google Scholar
• Ujevic, Z., Mohler, I., and Bolf, N. (2011). Soft Sensors for splitter product property estimation in CDU. Chem. Eng. Commun., 198, 1566–1578.
   Web of Science ®Google Scholar
• Vanhatalo, J., Riihimäki, J., Hartikainen, J., Jylänki, P., Tolvanen, V., and Vehtari, A. (2013). GPstuff: Bayesian modeling with Gaussian processes. J. Taiwan. Inst. Chem. Eng., 14, 1175–1179.
   Google Scholar
• Wahba, G. (1990). Spline models for observational data, SIAM, Philadelphia.
   Google Scholar
• Wan, J., Huang, M., Ma, Y., Guo, W., Wang, Y., Zhang, H., and Sun, X. (2011). Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system. Appl. Soft. Comput., 11, 3238–3246.
   Web of Science ®Google Scholar
• Wang, J.S., and Guo, Q.P. (2013). D-FNN based soft-sensor modeling and migration reconfiguration polymerizing process. Appl. Soft. Comput., 13, 1892–1901.
   Web of Science ®Google Scholar
• Wise, B. M., and Gallagher, N. B. (1996). The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 6(6), 329–348.
   Web of Science ®Google Scholar
• Xibilia, M. G., and Barbalace, N. (2004). Sulphur recovery unit modeling via stacked neural network. Paper presented at the proceedings of the IASTED International Conference on Applied Simulation and Modelling, in Rhodes, Greece, art No. 443–061, pp. 65–70.
   Google Scholar
• Yan, X. (2008). Modified nonlinear generalized ridge regression and its application to develop naphtha cut point soft sensor. Comput. Chem. Eng., 32, 608–621.
   Google Scholar
• Yan, W., Hu, S., Yang, Y., Gao, F., and Chen, T. (2011). Bayesian migration of Gaussian process regression for rapid process modeling and optimization. Chem. Eng. J., 166, 1095–1103.
   Web of Science ®Google Scholar
• Yuan, X., Ge, Z., and Song, Z. (2016). Spatio‐temporal adaptive soft sensor for nonlinear time‐varying and variable drifting processes based on moving window LWPLS and time difference model. Asia‐Pacific. J. Chem. Eng., 11, 209–219.
   Web of Science ®Google Scholar