[1]. Jin H., Chen X., Yang, J., Wu, L., (2014). Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes. Computers & Chemical Engineering. 71, 77–93. doi: 10.1016/j.compchemeng.2014.07.014. ##
[2]. Shokry, A., Vicente, P., Escudero, G., Pérez-Moya, M., Graells, M., Espuña, A., (2018), Data-driven soft-sensors for online monitoring of batch processes with different initial conditions, Computers & Chemical Engineering, 118(4), 159-179. doi: 10.1016/j.compchemeng.2018.07.014##
[3]. Kadlec, P., Gabrys, B., Strandt, S., (2009). Data-driven soft sensors in the process industry, Computers & Chemical Engineering, 33(4), 795-814. doi: 10.1016/j.compchemeng.2008.12.012##
[4]. Jiang, Y., Yin, S., Dong, J., Kaynak, O., (2021), A Review on Soft Sensors for Monitoring, Control and Optimization of Industrial Processes, IEEE Sensors Journal, 21(11), 12868–12881. Doi: 10.1109/JSEN.2020.3033153. ##
[5]. Kaneko H., Arakawa M., Funatsu K., (2009), Development of a new soft sensor method using independent component analysis and partial least squares, AIChE Journal, 55(1), 87–98. Doi: 10.1002/aic.11648. ##
[6]. Sliskovic D., Grbic R., Hocenski Z., (2011), Methods for plant data-based process modeling in soft sensor development, Automatika, 52(4), 306–318. Doi: 10.1080/00051144.2011.11828430. ##
[7]. صادق صمیمی، ا.پ.، اسفندیاری بیات، ع. و امامزاده، ا.، (1401)، تعیین مشخصات جریان‌های دو فازی نفت- آب توسط شبکه عصبی کانولوشنی جریانی، پژوهش نفت، (127)32، 80-65. Doi:10.22078/PR.2022.4895.3189. ##
[8]. Li, Z., Jin, H., Dong, S., Qian, B., Yang, B., Chen, X., (2022), Semi-supervised ensemble support vector regression based soft sensor for key quality variable estimation of nonlinear industrial processes with limited labeled data, Chemical Engineering Research and Design, 179, 510-526. Doi:10.1016/j.cherd.2022.01.026. ##
[9]. Wang. Z.-H., Li, Y.-T., Wen, F.-C., (2023), A Novel In-Line Polymer Melt Viscosity Sensing System of Integrated Soft Sensor and Machine Learning, IEEE Sensors Journal, 23(11), 12181 – 12189. Doi: 10.1109/JSEN.2023.3267682. ##
[10]. Zhang, X., Song, C., Zhao, J., Xia, D., (2023), Gaussian mixture continuously adaptive regression for multimode processes soft sensing under time-varying virtual drift, Journal of Process Control, 124, 1-13. Doi: 10.1016/j.jprocont.2023.02.003. ##
[11]. شکری، س.، صادقی، م.ت. و احمدی مروست، م.، (1392)، ارائه روش ترکیبی پیش‌پردازش داده‌ها در ماشین بردار رگرسیون جهت پیش‌بینی کیفیت گازوییل پالایش شده، پژوهش نفت، (75)23، 116-102، doi: 10.22078/PR.2013.317.. ##
[12]. Shao, W., Tian, X., (2015), Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models, Chemical Engineering Research and Design, 95, 113–132. Doi: 10.1016/j.cherd.2015.01.006. ##
[13]. Yeo, W.S., Saptoro, A., Kumar, P., Kano, M., (2023), Just-in-time based soft sensors for process industries: A status report and recommendations, Journal of Process Control, 128, 103025. Doi: 10.1016/j.jprocont.2023.103025. ##
[14]. Young, P.C., (1999), Nonstationary time series analysis and forecasting. Progress in Environmental Science, 1, 3-48. ##
[15]. Young, P.C., (2006), The data-based mechanistic approach to the modelling, forecasting and control of environmental systems. Annual Reviews in Control, 30(2), 169-182. Doi: 10.1016/j.arcontrol.2006.05.002. ##
[16]. Young, P.C., McCabe, A.P., Chotai, A., (2002), State-dependent parameter nonlinear systems: identification, estimation and control, IFAC Proceedings, 35(1), 441-446. Doi: 10.3182/20020721-6-ES-1901.00235. ##
[17]. Sadeghi, J., (2006), Modelling and control of non-linear systems using State-Dependent Parameter (SDP) models and Proportional-Integral-Plus (PIP) control method. Lancaster University: United Kingdom, Ph.D. Thesis. ##
[18]. بیدار، ب.، (1396)، طراحی حسگر نرم داده محور به‌منظور پیش‌بینی برخط کیفیت محصول در برج‌های تقطیر به‌روش پارامتر وابسته به متغیر حالت، دانشگاه سیستان و بلوچستان، زاهدان، ایران. ##
[19]. Tavakoli Dastjerd, F., Sadeghi, J., Shahraki, F., Khalilipour, M.M., Bidar, B., (2022), Soft sensor design using multi-state dependent parameter methodology based on generalized random walk method, IEEE Sensors Journal, 22(8). Doi: 10.1109/JSEN.2022.3147306. ##
[20]. Gharehbaghi, H., Sadeghi, J., (2016), A novel approach for prediction of industrial catalyst deactivation using soft sensor modeling, Catalysts, 6(7), 93-109. Doi: 10.3390/catal6070093. ##
[21]. Bidar, B., Sadeghi, J., Shahraki, F., Khalilipour, M.M., (2017), Data-driven soft sensor approach for online quality prediction using state dependent parameter models. Chemometrics and Intelligent Laboratory Systems, 162, 130-141. Doi: 10.1016/j.chemolab.2017.01.004. ##
[22]. Bidar, B., Khalilipour, M.M., Shahraki, F., Sadeghi, J., (2018), A data-driven soft-sensor for monitoring ASTM-D86 of CDU side products using local instrumental variable (LIV) technique, Journal of the Taiwan Institute of Chemical Engineers, 84, 49-59. Doi: 10.1016/j.jtice.2018.01.009. ##
[23]. Bidar, B., Shahraki, F., Sadeghi, J., Khalilipour, M.M., (2018), Soft sensor modeling based on multi-state-dependent parameter models and application for quality monitoring in industrial sulfur recovery process, IEEE Sensors Journal, 18(11), 4583–4591. Doi: 10.1109/JSEN.2018.2818886. ##
[24]. نعیمی، ف.، (1397)، طراحی حسگر نرم‌افزاری داده محور به‌منظورپیش‌بینی برخط کیفیت نفت خام در واحد نمک‌زدایی/ آب‌زدایی به روش پارامتر وابسته به متغیرحالت، دانشگاه سیستان و بلوچستان، زاهدان، ایران. ##
[25]. Bidar, B., Naimi Rad, F., Khalilipour, M.M., Shahraki, F., Sadeghi, J., (2020), Quality Soft Sensor Design for Crude Oil Desalting/Dehydration Unit Using Local Instrumental Variable (LIV) Approach, The 11th International Chemical Engineering Congress & Exhibition (IChEC 2020), Fouman, Iran. ##
[26]. حسن‌پور، ر.، (1398)، طراحی حسگر نرم داده محور با روش پارامتر وابسته به متغیر حالت به‌منظور پیش‌بینی کیفیت در فرآیند تنسی ایستمن، دانشگاه سیستان و بلوچستان، زاهدان، ایران. ##
[27]. حسن‌پور، ر.، خلیلی پور، م.م.، صادقی، ج.، بیدار، ب.، (1402)، ارزیابی کیفیت محصول مبتنی بر روش پارامتر وابسته به متغیر حالت با کاربرد در فرآیند تنسی ایستمن (TEP)، مجله کنترل، (1)17، 91-77. ##
[28]. Jiang, Y.,Yin, S., Dong, J., Kaynak, O., (2021), A review on soft sensors for monitoring, control, and optimization of industrial processes, IEEE Sensors Journal, 21(11), 12868 – 12881, 2021. Doi: 10.1109/JSEN.2020.3033153. ##
[29]. Wang, L. Jin, H., Chen, X., Dai, J., Yang, K.,Zhang, D., (2016), Soft sensor development based on the hierarchical ensemble of Gaussian process regression models for nonlinear and non-gaussian chemical processes. Industrial & Engineering Chemistry Research, 55(28), 7704–7719. Doi: 10.1021/acs.iecr.6b00240. ##
[30]. Frauendorfer, E., Wolf, A., Hergeth, W.D., (2010), Poly. ##
merization online monitoring, Chemical Engineer [31]. Rännar, S., MacGregor, J.F., Wold, S., (1998), Adaptive batch monitoring using hierarchical PCA, Chemometrics and Intelligent Laboratory Systems, 41(1), 73-81. Doi: 10.1016/S0169-7439(98)00024-0. ##
[32]. Li, C., Ye, H., Wang, G., Zhang, J., (2005), A recursive nonlinear PLS algorithm for adaptive nonlinear process modeling, Chemical Engineering and Technology, 28(2),141-152. Doi: 10.1002/ceat.200407027. ##
[33]. Ahmed, F., Nazir, S., Yeo, Y.K.Y., (2009), A new soft sensor based on recursive partial least squares for online melt index predictions in grade-changing hdpe operations, Chemical Product and Process Modeling, 4(1). Doi: 10.2202/1934-2659.1271. ##
[34]. Facco, P., Doplicher, F., Bezzo, F., Barolo, M., (2009), Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process, Journal of Process Control, 19(3), 520-529. Doi: 10.1016/j.jprocont.2008.05.002. ##
[35]. Facco, P., Bezzo, F., Barolo, M., (2010), Nearest-neighbor method for the automatic maintenance of multivariate statistical soft sensors in batch processing, Industrial & Engineering Chemistry Research, 49(5), 2336-2347. Doi: 10.1021/ie9013919. ##
[36]. Souza, F.A., Araújo, R., (2014), Mixture of partial least squares experts and application in prediction settings with multiple operating modes, Chemometrics and Intelligent Laboratory Systems, 130, 192-202. Doi: 10.1016/j.chemolab.2013.11.006. ##
[37]. Ferreira, V., Souza, F.A., Araújo. R., (2017), Semi-supervised soft sensor and feature ranking based on co-regularised least squares regression applied to a polymerization batch process, 15th International Conference on Industrial Informatics (INDIN), Emden, Germany. Doi: 10.1109/INDIN.2017.8104781. ##
[38]. Abeykoon, C., (2018), Design and applications of soft sensors in polymer processing: A review, IEEE Sensors Journal, 19(8), 2801-2813. Doi: 10.1109/JSEN.2018.2885609 . ##
[39]. Yin, Z., Hao, K., Chen, L., Cai, X., Zhu, X., (2019), Forecasting the intrinsic viscosity of polyester based on improved extreme learning machine, International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China. Doi: 10.1109/ICAICA.2019.8873494. ##
[40]. He, Y.-L., Tian, Y., Xu, Y., Zhu, Q.-X., (2020), Novel soft sensor development using echo state network integrated with singular value decomposition: Application to complex chemical processes, Chemometrics and Intelligent Laboratory Systems, 200, 103981. Doi: 10.1016/j.chemolab.2020.103981. ##
[41]. Zhu, X., Hao, K., Xie, R., Huang, B., (2021), Soft sensor based on extreme gradient boosting and bidirectional converted gates long short-term memory self-attention network, Neurocomputing, 434, 126-136. Doi: 10.1016/j.neucom.2020.12.028.
[42]. Zhu, X., Damarla, S.K., Hao, K., Huang, B., Chen, H., Hua, Y., (2023), ConvLSTM and Self-Attention Aided Canonical Correlation Analysis for Multioutput Soft Sensor Modeling, IEEE Transactions on Instrumentation and Measurement, 72. Doi: 10.1109/TIM.2022.3225004. ##
[43]. Perera, Y.S., Ratnaweera, D.A.A.C., Dasanayaka, C.H., Abeykoon, C., (2023), The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review, Engineering Applications of Artificial Intelligence, 121, 105988. Doi: 10.1016/j.engappai.2023.105988. ##
[44]. Scheirs, J., Long, T.E., (2005), Modern polyesters: Chemistry and technology of polyesters and copolyesters, John Wiley & Sons. ##
[45]. Deopura, B. L., Alagirusamy, R., Joshi, M., Gupta, b., (2008), Polyesters and polyamides, Woodhead Publishing in Textiles: Number 71, CRC press. ##
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