Dr Roland Dodd received his PhD in Information Systems Engineering from the Royal Melbourne Institute of Technology University in 2009. He has been a university academic at Central Queensland University (CQU) from 2001 to present. He is currently the Discipline Leader for Mathematics and Statistics and a Senior Lecturer in the School of Engineering and Technology at CQU.
His teaching over the past 17 years has covered Mathematics, Statistics, Data Communications, Networks, Database Use and Design, Database Application Development, Object Oriented Programming, Software Fundamentals, Dynamic Web Interfaces and Professional Issues in Computing. He has been the recipient of the multiple awards for excellence in learning and teaching, including recognition as the 2016 CQU Educator of the Year for his teaching in first year undergraduate mathematics.
His research interests include artificial intelligence, information systems engineering, industrial decision support systems, industrial process modelling and supervisory control systems in expert environments. He has more than 35 journal publications, conference papers, technical reports, thesis, government milestone reports, research presentations and prototyped intelligent systems, in his disciplinary areas.
As Chief Investigator leader he has secured grants, worth in excess of $700,000, in collaboration with industry partners. He is an editorial board member for the International Academy, Research and Industry Association International Journal on Advances in Intelligent Systems and the International Journal on Advances in Software. He was also a technical program committee member for 8 major international conferences and a special session track organiser for 37th Annual Conference of the IEEE Industrial Electronics Society.
Sugar mill process crystal clear thanks to artificial intelligence, 23 June, 2011, http://uninews.cqu.edu.au/UniNews/viewStory.do?story=8383
Recognising those who inspire our students, 08 March, 2011, http://uninews.cqu.edu.au/UniNews/viewStory.do?story=7893
Education
Dr Roland Dodd and Dr Andrew Chiou of CQU, Professor Xinghuo Yu (RMIT) and Professor Ross Broadfoot (QUT), were granted a Sugar Research and Development Corporation research grant to be undertaken between July 2011 to December 2014. The grant supports implementation of a supervisory control system for pan and fugal stations operations in Australian sugar mills. This system is based on an intelligent industrial decision support platform and industrial process models, developed by Dr Roland Dodd during his PhD research, with the grant focusing on implementation and commercialisation aspects of this technology within the sugar mill environment.
This project aims to demonstrate the viability of the developed framework through improved sugar productivity and quality, reduction of production costs, increasing equipment utilisation and improving the decision making process undertaken by factory staff within the sugar mill crystallisation stage and fugal operational areas. Live factory trials are to demonstrate the merits of the proposed technology with expected benefits conservatively estimated at $10,000,000 per annum when implemented in Australian sugar mills.
PhD (Information Systems Engineering), RMIT University
PhD research led to the development of a prototype knowledge based supervisory support system (KBSSS) for pan stage operations in a sugar mill to demonstrate proof of concept. To demonstrate the viability of the proposed KBSSS framework a prototype system was developed in accordance with the proposed framework. As a result of research and development carried, the KBSSS's test results demonstrated the viability of the proposed KBSSS framework and highlight the forecasting capabilities of the system resulting in favourable outcomes compared to data from pan stage operations. As a result of the research undertaken a prototype KBSSS, for pan stage operations, based upon the three core supporting intelligent system technologies, supporting the overall industrial control system framework, was developed.
Sugar Research and Development Corporation, National Competitive Research Grant
This research project aims to implement a knowledge based supervisory support system (KBSSS) for pan and fugal station operations and to demonstrate and evaluate its effectiveness and acceptability by factory operators, supervisors and management. The pan and fugal stations are considered to be an ideal location in the factory to initiate progress towards a factory wide supervisory/advisory system. This software development is based upon extension of PhD research prototyping.
The KBSSS will provide advice to supervisors and operators so that early decisions, such as changes to steam rates or allocation of pans to different duties, result in improved outcomes with respect to avoiding production rate difficulties, and maintaining good operational performance with respect to sugar quality, sugar recovery and minimisation and smoothing of steam consumption on the pan stage.
Industry implementation is being undertaken at Macknade sugar mill located in Ingham QLD with the support of Sucrogen industry partner.
Bachelor of Information Technology Honours (First Class), CQ University
Honours degree research led to the development of a fuzzy neural network for intelligent adaptive learning control of an inverted pendulum system. The research focused on examining outcomes for improving the learning capabilities of a fuzzy neural network system. The research proposed a modified adaptive rule importance learning algorithm based upon a fuzzy hierarchical error approach. A modified network structure that is more intuitive, powerful and melds directly into the fuzzy neural network system was developed. This system allows for fast online adaptive control while simultaneously performing system learning with the considerable feature of no offline training required. Furthermore, genetic algorithm optimisation of fuzzy rule neural network rule base and learning parameters was also undertaken.
Dodd, R. and Broadfoot, R., (2015). Milestone Report 7: Implement supervisory/advisory control of pan and fugal stations, Australian Government Sugar Research and Development Corporation, Project Number QUT038.
Dodd, R. and Broadfoot, R., (2014). Milestone Report 7: Implement supervisory/advisory control of pan and fugal stations, Australian Government Sugar Research and Development Corporation, Project Number QUT038.
Dodd, R. and Broadfoot, R., (2013). Milestone Report 6: Implement supervisory/advisory control of pan and fugal stations, Australian Government Sugar Research and Development Corporation, Project Number QUT038.
Dodd, R. and Broadfoot, R., (2012). Milestone Report 5: Implement supervisory/advisory control of pan and fugal stations, Australian Government Sugar Research and Development Corporation, Project Number QUT038.
Dodd, R. and Broadfoot, R., (2012). Milestone Report 4: Implement supervisory/advisory control of pan and fugal stations, Australian Government Sugar Research and Development Corporation, Project Number QUT038.
Dodd, R. (2011). Pan Stage Sucrose and Impurity Loadings: Macknade Sugar Mill 2006-2009 Seasons, internal report, CQUniversity.
Dodd, R. and Broadfoot, R., (2011). Milestone Report 3: Implement supervisory/advisory control of pan and fugal stations, Australian Government Sugar Research and Development Corporation, Project Number QUT038.
Dodd, R. and Broadfoot, R., (2010). Milestone Report 2: Implement supervisory/advisory control of pan and fugal stations, Australian Government Sugar Research and Development Corporation, Project Number QUT038.
I am currently accredited for supervision in the following:
At the level of Associate Supervisor
Chemical Engineering - Process Control and Simulation
Manufacturing Engineering - Manufacturing Processes and Technologies (excl. Textiles)
Artificial Intelligence and Image Processing - Expert Systems
Artificial Intelligence and Image Processing - Neural, Evolutionary and Fuzzy Computation
Computer Software - Software Engineering
Information Systems - Decision Support and Group Support Systems