Sensor-Less Predictive Drying Control of Pneumatic Conveying Batch Dryers

This paper presents predictive drying control design for a lab-scaled industrial pneumatic conveying dryer (PDC) involves with continuous/ batch processing of powder materials. The model predictive control (MPC) is an established method for drying control in various drying applications, such as fluidized bed dryers, rotary dryer, infrared dryer, timber dryers, baker’s yeast dryers and so on. But the predictive control of PDCs have not been studied in the literature, however, these dryers are widely used in food, agriculture, and chemical industries, particularly suitable for batch processing of fine grained materials. The unavailability of any suitable control oriented first principle’s model of these dryers make the predictive control design and implementation issues more challenging. Existing control methods for similar drying applications use outlet material moisture as a main control variable, online measurement, of which is difficult, costly, and unreliable due to the involvement of materials in granular/powder form. In the present contribution, an innovative control oriented model of the dryer is derived from first principle’s encompassing a soft sensor-based online powder-moisture measurement procedure replacing the physical moisture sensors. The proposed physical sensor-less powder moisture control strategy stands on the traditional two-layer predictive control paradigm involving detection of an economically best operating point for batch dryer operation by optimizing the various process economic objectives followed by employing a suitable state space MPC (SSMPC) law for steering the process to operate at economically best operating point. The developed control strategy has been implemented and tested under practical settings and shown its effectiveness in improving the drying performance and product quality compared with an inbuilt auto-tuned proportional integral plus derivative controller of Honeywell make HC900 programmable logic controller.

[1]  D. Grant Fisher,et al.  A state space formulation for model predictive control , 1989 .

[2]  Mustafa Türker,et al.  Dynamic Neural-Network-Based Model-Predictive Control of an Industrial Baker's Yeast Drying Process , 2008, IEEE Transactions on Neural Networks.

[3]  R. Arjona,et al.  Automation of an olive waste industrial rotary dryer , 2005 .

[4]  José Teixeira Freire,et al.  Drying of Coarse Particles in a Vertical Pneumatic Conveyor , 2007 .

[5]  H. Ramon,et al.  MPC as control strategy for pasta drying processes , 2009, Comput. Chem. Eng..

[6]  James B. Rawlings,et al.  Model predictive control with linear models , 1993 .

[7]  Jianzhong Zhou,et al.  Parameters identification of nonlinear state space model of synchronous generator , 2011, Eng. Appl. Artif. Intell..

[8]  Wolfgang Marquardt,et al.  Integration of Model Predictive Control and Optimization of Processes: Enabling Technology for Market Driven Process Operation , 2000 .

[9]  Pascal Dufour,et al.  Control Engineering in Drying Technology: Review and Trends , 2006 .

[10]  Biplab Satpati,et al.  Modeling Identification and Control of an Air Preheating Furnace of a Pneumatic Conveying and Drying Process , 2014 .

[11]  Dennis Eichmann,et al.  Drying And Storage Of Grains And Oilseeds , 2016 .

[12]  Hamidreza Modares,et al.  System Identification and Control using Adaptive Particle Swarm Optimization , 2011 .

[13]  Lennart Ljung,et al.  Subspace identification from closed loop data , 1996, Signal Process..

[14]  Carlos Bordons,et al.  Model based predictive control of an olive oil mill , 2008 .

[15]  Arun S. Mujumdar,et al.  Application of Artificial Neural Networks (ANNs) in Drying Technology: A Comprehensive Review , 2015 .

[16]  Arun S. Mujumdar,et al.  Trends in Modeling and Sensing Approaches for Drying Control , 2014 .

[17]  Susanne Zaglauer,et al.  Design of Experiments for nonlinear dynamic system identification , 2011 .

[18]  A. Mujumdar Handbook of Industrial Drying , 2020 .

[19]  Roderick Melnik,et al.  Model-based analysis and simulation of airflow control systems of ventilation units in building environments , 2007 .

[20]  Lennart Ljung,et al.  Estimate Physical Parameters by Black Box Modeling , 2003 .

[21]  Dorin Sendrescu,et al.  Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization , 2013 .

[22]  Lennart Ljung,et al.  Perspectives on system identification , 2010, Annu. Rev. Control..

[23]  Johan U. Backstrom,et al.  Quadratic programming algorithms for large-scale model predictive control , 2002 .

[24]  Gilles Trystram,et al.  Neural networks for the heat and mass transfer prediction during drying of cassava and mango , 2004 .

[25]  Gilles Trystram,et al.  APPLICATION OF FUZZY RULES-BASED MODELS TO PREDICTION OF QUALITY DEGRADATION OF RICE AND MAIZE DURING HOT AIR DRYING , 1998 .

[26]  Anders Rasmuson,et al.  Mathematical model of a pneumatic conveying dryer , 1997 .

[27]  Håkan Hjalmarsson,et al.  For model-based control design, closed-loop identification gives better performance , 1996, Autom..

[28]  B. De Moor,et al.  Closed loop subspace system identification , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[29]  B. Pitchumani,et al.  Studies on gas–solid heat transfer during pneumatic conveying , 2008 .

[30]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[31]  Andrzej Kmiec,et al.  MODELLING GAS-SOLID FLOW IN A PNEUMATIC-FLASH DRYER , 1997 .

[32]  Gilles Trystram,et al.  Optimal constrained non-linear control of batch processes: Application to corn drying , 1997 .

[33]  Helge Didriksen,et al.  Model based predictive control of a rotary dryer , 2002 .

[34]  Biplab Satpati,et al.  Online estimation of rice powder moisture in a pneumatic conveying dryer , 2016, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT).

[35]  Wenfu Wu,et al.  A neural network for predicting moisture content of grain drying process using genetic algorithm , 2007 .

[36]  Abdollah Homaifar,et al.  Constrained Optimization Via Genetic Algorithms , 1994, Simul..

[37]  Helen Durand,et al.  A tutorial review of economic model predictive control methods , 2014 .

[38]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[39]  H. Abukhalifeh,et al.  Model Predictive Control of an Infrared-Convective Dryer , 2005 .

[40]  Bart De Moor,et al.  Subspace Identification for Linear Systems: Theory ― Implementation ― Applications , 2011 .

[41]  Pascal Dufour,et al.  Infrared Drying Process of an Experimental Water Painting: Model Predictive Control , 2004 .

[42]  Paul M.J. Van den Hof,et al.  Closed-Loop Issues in System Identification , 1997 .

[43]  Hsiao-Fan Wang,et al.  A closed-loop logistic model with a spanning-tree based genetic algorithm , 2010, Comput. Oper. Res..

[44]  Pascal Dufour,et al.  On nonlinear distributed parameter model predictive control strategy: on-line calculation time reduction and application to an experimental drying process , 2003, Comput. Chem. Eng..

[45]  Lennart Ljung,et al.  Closed-loop identification revisited , 1999, Autom..

[46]  Griffiths G. Atungulu,et al.  Mathematical modeling of pneumatic drying of rice powder , 2008 .

[47]  H. E. Musch,et al.  Nonlinear model predictive control of timber drying , 1998 .

[48]  Qiang Liu,et al.  A model-predictive controller for grain drying , 2001 .

[49]  Liulin Cao,et al.  A novel closed loop identification method and its application of multivariable system , 2012 .

[50]  D. P. Sekulic,et al.  Fundamentals of Heat Exchanger Design , 2003 .

[51]  M. Haloua,et al.  State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints , 2012 .

[52]  José A. Ramos,et al.  Identification of Parameterized Gray-Box State-Space Systems: From a Black-Box Linear Time-Invariant Representation to a Structured One , 2014, IEEE Transactions on Automatic Control.

[53]  A. H. Pelegrina,et al.  Modelling the pneumatic drying of food particles , 2001 .

[54]  Thomas F. Edgar,et al.  Process energy systems: Control, economic, and sustainability objectives , 2012, Comput. Chem. Eng..

[55]  Jane E. Sargison,et al.  Modelling and simulation of Paddy Grain (Rice) drying in a simple pneumatic dryer , 2007 .

[56]  Hassan Hammouri,et al.  Model predictive control during the primary drying stage of lyophilisation , 2010 .

[57]  Wenfu Wu,et al.  Parameters Online Detection and Model Predictive Control during the Grain Drying Process , 2013 .

[58]  Pekka Ahtila,et al.  Simulation Model for the Model-Based Control of a Biofuel Dryer at an Industrial Combined Heat and Power Plant , 2006 .

[59]  K. Valarmathi,et al.  Nonlinear Modeling of Moisture Control of Drying Process in Paper Machine , 2012 .

[60]  J. W. Robinson,et al.  Improve dryer control , 1992 .

[61]  Rekha S. Singhal,et al.  Basmati rice: a review , 2002 .

[62]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[63]  H. Abukhalifeh,et al.  Model predictive control of an infrared-dryer , 2003, 2003 IEEE International Workshop on Workload Characterization (IEEE Cat. No.03EX775).

[64]  R. D. Radford,et al.  A model of particulate drying in pneumatic conveying systems , 1997 .

[65]  Claudio Altafini,et al.  Robust control of a flash dryer plant , 1997, Proceedings of the 1997 IEEE International Conference on Control Applications.

[66]  Mustafa Turker,et al.  Nonlinear predictive control of a drying process using genetic algorithms. , 2006, ISA transactions.