Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

In this paper, an attempt is made to develop a bio-inspired soft sensor as a real-time identifier for online estimations of the engine-out hydrocarbon emission and exhaust gas temperature over the coldstart operation of an automotive engine. To date, a number of studies have been done to design soft models as offline identifiers for capturing the knowledge of a set of collected coldstart databases. The results indicated that bio-inspired computation can be of great use for the identification of coldstart phenomenon. It also can be a good alternative to classic approaches. However, bio-inspired soft sensors have not been used as online estimators for monitoring the behavior of coldstart phenomenon. This is while it is desirable to have a flexible, inexpensive online identifier which can be employed to detect the real-time behavior of the engine over coldstart periods. Here, an exhaustive numerical study is conducted to demonstrate the potentials of bio-inspired approaches for online measuring of engine-out hydrocarbon emission and exhaust gas temperature during the coldstart operation. To do so, a set of well-known bio-inspired optimizers are combined with a time-delayed artificial neural network to design an evolvable soft sensor. The results clearly demonstrate the high potential of bio-inspired computation for handling the difficulties associated with the real-time identification of engine-out hydrocarbon emission and exhaust gas temperature.

[1]  Yan Sun,et al.  Designing a Soft Sensor with the Weighted Fuzzy Neural Network , 2012 .

[2]  Ahmad Mozaffari,et al.  Optimal design of classic Atkinson engine with dynamic specific heat using adaptive neuro-fuzzy inference system and mutable smart bee algorithm , 2013, Swarm Evol. Comput..

[3]  Saeed Behzadipour,et al.  The great salmon run: a novel bio-inspired algorithm for artificial system design and optimisation , 2012, Int. J. Bio Inspired Comput..

[4]  Maria del Carmen Pegalajar Jiménez,et al.  A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference , 2005, Pattern Recognit..

[5]  Pantelis N. Botsaris,et al.  An estimation of three-way catalyst performance using artificial neural networks during cold start , 2003 .

[6]  Pannag R. Sanketi,et al.  Coldstart modeling and optimal control design for automotive SI engines , 2009 .

[7]  Pierantonio Facco,et al.  Nearest-Neighbor Method for the Automatic Maintenance of Multivariate Statistical Soft Sensors in Batch Processing , 2010 .

[8]  A. J. Morris,et al.  Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process , 2003 .

[9]  Hui Shao,et al.  Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms , 2006, Soft Comput..

[10]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[11]  Sandip Kumar Lahiri,et al.  Development of a hybrid support vector machine and genetic algorithm model for regime identification of slurry transport in pipelines , 2010 .

[12]  Nasser L. Azad,et al.  Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission , 2015, J. Exp. Theor. Artif. Intell..

[13]  Nasser L. Azad,et al.  Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification , 2014, Neurocomputing.

[14]  K. Suresh Manic,et al.  Firefly Algorithm with Various Randomization Parameters: An Analysis , 2013, SEMCCO.

[15]  Ahmad Lotfi,et al.  Soft computing applications in dynamic model identification of polymer extrusion process , 2004, Appl. Soft Comput..

[16]  Ahmad Mozaffari,et al.  Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature , 2014, Appl. Soft Comput..

[17]  Lúcia Valéria Ramos de Arruda,et al.  A neuro-coevolutionary genetic fuzzy system to design soft sensors , 2008, Soft Comput..

[18]  Nasser L. Azad,et al.  Determining Model Accuracy Requirements for Automotive Engine Coldstart Hydrocarbon Emissions Control , 2012 .

[19]  Pierantonio Facco,et al.  Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process , 2009 .

[20]  Sandip Kumar Lahiri,et al.  Novel soft sensor modeling and process optimization technique for commercial petrochemical plant , 2009 .

[21]  Duc Truong Pham,et al.  Training Elman and Jordan networks for system identification using genetic algorithms , 1999, Artif. Intell. Eng..

[22]  Wai Kean Yap,et al.  Exhaust emissions control and engine parameters optimization using artificial neural network virtual sensors for a hydrogen-powered vehicle , 2012 .

[23]  Yan Sun,et al.  Soft Sensor Modeling Based on Fuzzy System Optimization , 2012 .

[24]  W. Cholewa,et al.  Fault Diagnosis: Models, Artificial Intelligence, Applications , 2004 .

[25]  Yu Jinshou,et al.  The Kalman Particle Swarm Optimization Algorithm and Its Application in Soft-sensor of Acrylonitrile Yield , 2005 .

[26]  Saeed Behzadipour,et al.  Identifying the tool-tissue force in robotic laparoscopic surgery using neuro-evolutionary fuzzy systems and a synchronous self-learning hyper level supervisor , 2014, Appl. Soft Comput..

[27]  Ahmad Mozaffari,et al.  Ensemble mutable smart bee algorithm and a robust neural identifier for optimal design of a large scale power system , 2014, J. Comput. Sci..

[28]  Nasser L. Azad,et al.  An ensemble neuro-fuzzy radial basis network with self-adaptive swarm based supervisor and negative correlation for modeling automotive engine coldstart hydrocarbon emissions: A soft solution to a crucial automotive problem , 2015, Appl. Soft Comput..

[29]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..

[30]  Tiina M. Komulainen,et al.  Fault detection and isolation of an on-line analyzer for an ethylene cracking process , 2008 .

[31]  Victor Wouk.  HYBRID ELECTRIC VEHICLES , 1997 .

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

[33]  Wai Kean Yap,et al.  Comparative analysis of artificial neural networks and dynamic models as virtual sensors , 2013, Appl. Soft Comput..

[34]  Wei-Der Chang.  Differential evolution-based nonlinear system modeling using a bilinear series model , 2012, Appl. Soft Comput..

[35]  Marzuki Khalid,et al.  Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm , 2011, Neural Computing and Applications.

[36]  Silvia Curteanu,et al.  Multi-objective optimization of a stacked neural network using an evolutionary hyper-heuristic , 2012, Appl. Soft Comput..

[37]  Weihua Li,et al.  Recursive PCA for adaptive process monitoring , 1999 .

[38]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[39]  Nasser L. Azad,et al.  A robust safety-oriented autonomous cruise control scheme for electric vehicles based on model predictive control and online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor , 2015, Neurocomputing.

[40]  Can Çinar,et al.  Artificial neural network based modeling of heated catalytic converter performance , 2005 .

[41]  Rui Araújo,et al.  Genetic fuzzy system for data-driven soft sensors design , 2012, Appl. Soft Comput..

[42]  Bogdan Gabrys,et al.  Review of adaptation mechanisms for data-driven soft sensors , 2011, Comput. Chem. Eng..

[43]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[44]  John McPhee,et al.  A battery hardware–in–the–loop setup for concurrent design and evaluation of real–time optimal HEV power management controllers , 2013 .

[45]  Matthew B. Blaschko,et al.  Discovering predictors of mental health service utilization with k-support regularized logistic regression , 2016, Inf. Sci..

[46]  Ahmad Mozaffari,et al.  Optimal design of constraint engineering systems: application of mutable smart bee algorithm , 2012, Int. J. Bio Inspired Comput..

[47]  Nasser L. Azad,et al.  Real-time predictive control strategy for a plug-in hybrid electric powertrain , 2015 .

[48]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[49]  Mehdi Shahbazian,et al.  The Design of Robust Soft Sensor Using ANFIS Network , 2014 .

[50]  Nasser L. Azad,et al.  Sliding mode control with bounded inputs and its application to automotive coldstart emissions reduction , 2012, 2012 American Control Conference (ACC).

[51]  Alireza Fathi,et al.  Preferred design of recurrent neural network architecture using a multiobjective evolutionary algorithm with un-supervised information recruitment: a paradigm for modeling shape memory alloy actuators , 2014 .

[52]  Guochu Chen,et al.  Cultural Particle Swarm Optimization Neural Network and Its Application in Soft-Sensing Modeling , 2009, 2009 Fifth International Conference on Natural Computation.

[53]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[54]  J. Karl Hedrick,et al.  A High Level approach to mean value modeling of an automotive engine during cold-start , 2014, 2014 American Control Conference.

[55]  Stephen A. Billings,et al.  Properties of neural networks with applications to modelling non-linear dynamical systems , 1992 .

[56]  Chia-Feng Juang,et al.  Recurrent fuzzy system design using elite-guided continuous ant colony optimization , 2011, Appl. Soft Comput..

[57]  Nasser L. Azad,et al.  Component sizing of a plug-in hybrid electric vehicle powertrain, Part A: coupling bio-inspired techniques to meshless variable-fidelity surrogate models , 2013, Int. J. Bio Inspired Comput..

[58]  Vladimir Bobal,et al.  Digital Self-tuning Controllers: Algorithms, Implementation and Applications , 2005 .

[59]  S. Billings,et al.  Correlation based model validity tests for non-linear models , 1986 .

[60]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[61]  Qidi Wu,et al.  Mussels Wandering Optimization: An Ecologically Inspired Algorithm for Global Optimization , 2012, Cognitive Computation.

[62]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[63]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[64]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[65]  Witold Pedrycz,et al.  Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization , 2011, Inf. Sci..

[66]  Marcin Witczak.  Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches , 2007 .

[67]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[68]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[69]  Theodora Kourti,et al.  Process analysis and abnormal situation detection: from theory to practice , 2002 .

[70]  J. K. Hedrick,et al.  Automotive engine hybrid modelling and control for reduction of hydrocarbon emissions , 2006 .

[71]  Jie-Sheng Wang,et al.  Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm , 2015, Comput. Intell. Neurosci..