M.J. Eichinger; B. Geraghty SYDAC Pty Ltd; Transfield Services

Transcription

1 Multi-purpose Reconfigurable Simulations for Rail Operations Research and Operator Training - A Case Study of the Challenges of the Introduction of Simulation into the Sugar Industry M.J. Eichinger; B. Geraghty SYDAC Pty Ltd; Transfield Services michael.eichinger@sydac.com.au; B.Geraghty@mkysugar.com.au Abstract: Innovative approaches are required to broaden the commercial adoption of simulation beyond the established markets of the defence and aerospace industries. Substantial opportunities exist and excellent returns on investments can be delivered in industries requiring operational cost reductions and efficiency gains. Staged delivery of simulations with the fidelity tuned to the achieving the organisations specific goals can deliver immediate benefits. Delivering simulation models with interfaces allowing different classes of users to interact with the same model facilitates access to their expert knowledge and supports multi-purpose use. This reduces the costs of tuning and validation increases the return on investment and develops a team spirit that is invaluable in both arriving at simulated solutions and achieving their successful implementation. The introduction of simulation technology to Mackay Sugar offers a story of success and further insight into it achievement. 1. INTRODUCTION Although simulation is well established in the defence and aerospace industries, broadening the commercial adoption of simulation still requires simulation developers to continue to create innovative approaches for the introduction of simulators and simulations to new markets. This is particularly true when targeting industries with limited funds and little exposure to simulation. Simulation developers still face customers with a lack of understanding of the technology or how to apply it successfully. They can be sceptical of the applicability of simulation to their industry and question whether it can accurately simulate their system or whether it will deliver useful, value adding solutions. They are concerned about getting an adequate return on their investment, how much they must initially invest to ensure a return and how long will it take to generate a return. There are also implementation concerns associated with overcoming fears and establishing the confidence of the customer s user base and stakeholders This paper uses the introduction of rail simulation into the Australian Sugar Industry as a case study. The Sugar Industry has, over several years, been facing low sugar prices resulting in the necessity to cut costs and increase productivity. As a result it has limited funds to invest in new technologies. This situation offers both significant budgetary challenges for developing simulations but also offers excellent opportunities due to the necessity to find efficiency gains. 1.1 The simulation multi-purpose reuse philosophy Simulation is a technology that allows its practitioners to create models that capture and represent knowledge of the characteristics, behaviours and performance of people, processes and machines in their environments. Simulator and simulations are tools that aid their users in experimenting with alternative options, learning about the simulated systems and/or predicting the results of various design changes or operational decision and actions. Obtaining, verifying and validating the underlying information as well as any resulting models and simulations is a substantial investment and hence to deliver the maximum opportunity for a return on investment effort should be made to ensure its reuse either by itself or in combination with other systems for as wide a range of used as possible. This is the driving philosophy behind the development of multi-purposed simulations as discussed here. 1.2 The non-technical challenges While defence has been a major sponsor of efforts to develop simulation technologies and standards to support reuse and combined simulations, the number of examples of the commercially successful applications outside defence is still few in number, particularly in smaller organisations in Australia facing strong competitive pressures (such as the sugar industry). While most of the evolution of simulation is based on the technology aspects, the importance of many of the nontechnical aspects (the human dimension) is sometimes overshadowed. Here the human dimension is not intended to mean the replication of the simulated equipments man machine interface but rather the knowledge of simulation and its use and the response of the involved parties to its introduction and use. This covers issues such as an understanding of: its capabilities and limitations; the issues associated with developing and implementing simulations; the ease of use, the effectiveness of the communication of the results to the intended user; user resistance to the new technology or the application of its results; the confidence, or lack thereof, or over-confidence; and how to benefit from the results.

2 2. CASE STUDY MACKAY SUGAR 2.1 Customer/Industry background Over the last 15 years the sugar price has dropped by approximately AUD$150/tonne. Recent poor rainfall has further reduced yields, further reducing revenues. As a result there has been an ongoing requirement to reduce cost and increase efficiencies. The tough market and declining revenue streams have ensured any capital expenditure is closely scrutinized and must offer a very favourable return on investment. Risk levels must also be acceptably low. Transport logistics is a key factor in industry efficiency, as it takes about 7 tonnes of cane to produce 1 tonne of raw sugar. Also sugar yields are significantly impacted if the cane is not harvested at the optimum time and processed within 16 hours of cutting. Delays in harvesting or between harvesting and processing ultimately results in reduced revenue to the grower. Historically the industry solution has been to construct a large number of relatively small mills near to the growers, figure 1. Figure 1: Mackay Sugar Mill & Track Network The investment required to upgrade mills with new technologies that provide more efficient use of cane and its by products combined with increasing labour costs has led to, an economy of scale driven, reduction in the number of mills, which continues today. Due to the large volumes of cane (approximately 7.5 million tonnes annually) that must be moved in a short period of time (typically 22 weeks), over increasing distances (800 km of track network), to fewer mills, narrow gauge rail has proven to be the most economic method of transport. The seasonal nature of the processing industry means that driving cane trains is a part time (seasonal) job. Cane trains, are however, not easy to drive. Configurations can consist of up to 400 four tonne bins, hauled by two 40 tonne distributed power locomotives with a remote brake van to assist in braking, figure 2. As a result derailments, have in the past, been too common and wear and tear on vehicles too high. Figure 2: Mackay Sugar Cane Train 2.2 Initial introduction of simulation As part of Mackay Sugar s ongoing effort to improve efficiencies and reduce cost, transport of cane was targeted. Sydac approached the Sugar Research Institute in 1997 offering rail simulation technology for cane train driver training. The simulator offered, was to be primarily for new driver training and improving driving methods to reduce fuel consumption and wear and tear (due to poor train dynamics). After numerous discussions, extending for over a year, the proposal had generated considerable interest from the various mills involved but had failed to be taken up. At this time Sydac s understanding of the Sugar Industry was limited and the Sugar Industry s understanding of rail simulation technology was similar. In 1999 Mackay Sugar, after considerable discussions with QR regarding their use and experiences with train driving simulators, procured access to (Sydac developed) QR train simulators, figure 3. In the first year Mackay Sugar put 25% of their drivers though a train management program [1], [2]. The aim was to reduce the number of major train dynamic related derailments 1. Mackay Sugar achieved an immediate reduction in the number of such derailments. As the cost of a single major derailment can be exceed $100k and the cost of the simulator based training at QR was approximately $70k, the investment paid off. The following year the remainder of the drivers were put through the program. Not one major train dynamic derailment occurred that year, confirming the value of the simulator based training investment. In the first two years of use, driver-training simulators had easily justified the investment made. Based on this success Mackay Sugar felt it could justify the purchase of its own simulator. 2.3 Review of initial simulator training From a technical perspective the key simulation goals are typically the achievement of the highest possible simulation fidelity, both in terms of the vehicle performance and the accuracy of replication of the driver s environment. In the case of the Mackay Sugar driver training program, the simulator used was a QR 1 A major train dynamic derailment is considered by Mackay Sugar as a derailment of wagons (cane bins) and/or damage to track which results in a track closure of more than 12 hours.

3 3000 class (diesel electric) locomotive simulator, designed to be configured to simulate rakes of up to 200 conventionally air braked wagons hauled by one or more 3000 class locomotives. Mackay Sugar s locomotives are diesel hydraulic, they are substantially lighter in weight, operate on a much narrower rail gauge, and haul very light wagons that have no brakes. Also the layout of the driver s controls is significantly different, as the driver typically sits facing the side window, allowing both a forward and rear view. Figure 3: Sydac built QR 3000 Class Simulator used for initial Mackay Driver Training Some adaptation of the locomotive model and a wagon model was made to better match the sugar train performance characteristics, but these were very basic changes. No changes were made to the driver s controls and no cane track routes were developed. From discussion with Mackay Sugar and QR the following were considered to be the key reasons for success. Mackay Sugar s investment in professional driver training demonstrated to the drivers, for the first time, that they were recognized as skilled professionals and essential people with a great deal of responsibility. The employment of simulators, which were seen as sophisticated modern and innovative technology, to address driving issues further built on driver s realisation that Mackay Sugar was taking the role of drivers very seriously. The training, encouraged by the training program employed, generated a lot of discussion within driver ranks. These discussions allowed the more experienced driver s to share their knowledge with less experienced operators. Unbeknown to them, it encouraged experienced driver s to become mentors to the less experienced. Although the train simulation was not a high fidelity simulation of the cane trains used by Mackay Sugar, the fundamental principle of train dynamics and the associated desirable driving characteristic were largely the same. Hence, although not immediately obvious, the simulator fidelity was sufficient for the intended training purpose. Hence we can conclude that by engaging with the drivers as professionals and by providing a serious training program, led to individual and organizational outcomes which were considered to be at least equally important to the simulation technology aspects. 2.4 Establishing the requirements for the ongoing simulation program Mackay Sugar was conscious of the need to, maintain the operational gains achieved and the enthusiasm and support of all involved parties, for the driver-training program. They sought to continue to build on the driver involvement and ownership developed in the initial training program. All driver s at the completion of training, were provided an opportunity to submit their thoughts on the simulator via a questionnaire. The basis of the questionnaire was, If Mackay Sugar had its own simulator, what do you think it should be capable of? The top five responses were: We need to be able to drive our own trains, not QR trains; We need to be able to drive on our own tracks; We should have a brake van to use on the trains; We should be able to look back over the train just like we do on the real ones; It would be good to train in wet weather and at night. These suggestions along with the base simulation capabilities, as found in the QR simulator, formed the Mackay Sugar simulator requirement. 2.5 Design, development and implementation approach In addition to Mackay Sugar s documented requirements Sydac recognized: The need to maintain the momentum and success already achieved by Mackay Sugar; That ideally the level of investment and cash flow needed to purchase a simulator needed to be of the same order as the cost of purchasing simulator time from QR; Neither Mackay Sugar or the SRI had or were likely to have access to detailed information on cane train performance; That there was the potential to address operational and validation issues by multipurposing the simulation models; The cost of extensive trials purely to obtain and validate train performance data would be prohibitive; The need to address the human psychological aspect of the introduction of new technology and technology that proposed change; The need to continue to support and expand the Mackay Sugar inclusive, team based use of simulation. To address these requirements a staged simulation/ simulator development and implementation process was proposed and accepted. A three-stage program was developed with the aim of:

4 Ensure each stage costs were of the same order as the previous simulator training costs; Providing a deliverable at each phase that was capable of being used by the customer to generate a suitable return on investment (and hence maintain management support); Provide a mechanism to obtain qualitative and quantitative information and feedback from drivers, trainers, rail operations and engineering support staff to allow subsequent stage deliverables to be refined; and To provide the opportunity engage interested parties to assist in gaining acceptance and user ownership. 2.6 Stage 1 Operations Simulator The first deliverable was a multi-purpose rail operations simulator. To allow rail operations, tuning and validation simulations to be carried out by technical staff, the capability to run faster than real-time from input data files and send outputs to graphs and data files was provided, figure 4. Figure 4: Stage 1 Deliverable - Rail Operations Simulator Data Interface For obtaining driver feedback the ability to drive in real time, using screen based driver controls and a display showing the vehicle located on gradient and curvature graphs of the track, was provided, see figure 5. It did not provide driver s physical controls, out-the-window visuals or trainee management, assessment or instructor tools. Due to the above limitations, the stage 1 version was not intended for driver training only for driver feedback for tuning and validation purposes. Operations simulations were also initially confined to comparative simulations. During this period Mackay Sugar still made use of QR simulators to maintain continuity of training for the bulk of the drivers. Figure 5: Stage 1 Deliverable - Rail Operations Simulator Driving Interface The simulation's multi-purpose capabilities did allow the customer to undertake rail operations research and engineering investigations [3] into: load limits, the employment of alternative train configurations for various tracks; the application of distributed power 2 and the use of remote brake vans. It allowed a pilot group of experienced drivers and operations staff to experiment with simulating existing train configurations operating on well-known track segments. This allowed the group to learn about using the simulator and the range of output data it could produce. It also allowed them to provide valuable tuning feedback by comparing the simulator predicted speeds and other parameters with their knowledge based on their years of experience. This process proved very successful in developing a development team spirit (which included the supplier and customer groups) which could focus resources on refining and validating the simulation where needed to provide maximum value to the customer, early in the process. This process was successful in develop growing confidence of both driver and management and support within the customer s user base. The simulation tuning based on largely qualitative driver feedback proved to be very effective with the simulation results predicting the trial results to an accuracy of approximately 3%. The cost associated with validation trials were also minimised. The results of the rail operations simulation also led to identification of alternative train configurations and alternative routes. This resulted in reductions in manning (by using fewer larger trains), reduction in train numbers on congested tracks, and significant associated cost savings (saving of approximately twice the simulator cost in the first year). The implementation process by allowing preliminary comparative investigation of potential alternative train configurations to be carried out, pre-final validation trials, allowed quantitative validation to be carried out through multipurpose tests that provided both data for validation 2 Distributed power is the use of remotely controlled locomotives a different location down the train

5 as well as proving the feasibility of the previously proposed new operational configurations. Driver training on new train configurations; Annual safe-working training. 2.7 Stage 2 Driver Training Simulator The stage 2 delivery provided computer generated outthe-window visuals (front window only), a physical replica of the cab controls, features for fault training and various student management, assessment and reporting tools. Reuse of the tuned rail models from the operations simulation in the full driver-training simulator guaranteed the accuracy of the simulator. It also provided confidence to drivers and operations planning staff that the results of their operational investigation, driving strategy and driver training simulations runs were and would be consistent. Due to the guaranteed consistency of the simulation results, the stage 2 simulator provided an excellent vehicle for drivers to develop driving strategies for the new train/route combinations and provide feedback in a common quantitative format (simulation log and output files) to rail operations planners. The confidence established between rail operations planning simulations and driver training simulations was a key factor in ensuring that new operation proposals were adopted and the associated gains achieved. The engagement of drivers in the simulation tuning and refinement and rail operations feedback loop also gave the drivers more confidence in the simulation. This combined with the action based learning training approach [1] minimized issues with driver adoption of simulator training. Simulator based driver training is attributed to having reduced the number of major train dynamic induced derailments from 10 in one year to 1. The significant saving made through incident reduction and resulting from operational investigations provided an excellent return on investment. The broad acceptance achieved by all stakeholders has resulted in further developments and expansion of the application of simulation in both Mackay Sugar and other sugar industry producers. 2.8 Stage 3 Rear Vision & Minor Enhancements The final stage, figure 6 added out-the-rear-window vision, enabling the driver to view the rake of following wagons and in some situations the track behind the train. Additional track database and track building tools were also provided. This stage also provided for additional fine-tuning based on the results of additional operational experience including trials that had been carried out to verify the feasibility of simulation predicted train configuration or operational changes during the simulator development. 2.9 Current Status The completed simulator is currently being used for: Investigating alternate train configurations; Testing current load limits; Developing new load limits in a safe environment; Figure 6: Current Mackay Sugar Cane Train Simulator Mackay Sugar is now convinced of the value of simulation to their business and has been instrumental in promoting its benefits to other sugar industry members. Mackay Sugars return on investment has substantially exceeded their original hopes. 3. CONCLUSIONS Access to QR, an existing user of driver training rail simulators, assisted in the initial assessment of the potential benefits of simulation technology to the Mackay Sugar situation and was an important factor in overcoming project risk concerns. Identifying and requiring only the level of fidelity necessary to achieve the required outcome allowed the use of a simulator that a face value might not have appeared appropriate for the task. This made the use of the QR simulator feasible and reduced the cost of the final simulator purchase. The ability to access a rail simulator and a well structured training program that addressed the key people factors as well the specific training goal (reducing train dynamic derailments) reduced the initial investment required and risks. This also helped ensure the delivery of immediate success using simulation. Based on the success with the QR simulator (build by Sydac) Mackay Sugar were able to justify the purchase of their own simulator on the basis of reducing major train dynamic derailments. Although Mackay Sugar's early success was achieved using a QR simulator that provided the required functionality to teach the required skills to the drivers, the most common request from drivers was for a simulator based on their trains and operating environment. As a result these items formed the basis for the additional requirement beyond those identified as necessary element of the QR simulators. Responding to user (driver) request, in stage 2, was an important factor in maintaining user support, which has been a key factor in the overall program success. The stage 1 delivery of an operations simulator without the realistic driver controls and OTW vision of the QR

6 simulator did raise some early concerns regarding the value of the stage 1 deliverable, which needed careful management. The stage 1 delivery of an operations simulator that could also be driven by a driver provided Sydac a method of delivering a simulator based on limited vehicle performance with a method of gaining useable tuning feedback from experienced drivers based on their experience and qualitative knowledge. At the same time it provided a mechanism and incentive for the customer, through carrying out operations investigations, to identify additional potential savings and justify trials to evaluate the feasibility of the new operational regimes while at the same time assisting in tuning and validating the simulator. This proved very cost effective and successful. Mackay Sugar justified purchasing a simulator solely on the QR experience of reducing train dynamic derailments. The Sydac delivered simulator delivered on this and although train dynamic training is ongoing, other benefits currently being delivered by the simulator are providing greater return on investment to Mackay Sugar. On introduction of the driver-training simulator to Mackay Sugar scepticism and paranoia were rampant! Most drivers feared both the unknown and the technology. To hide their fears most were quick to write off the simulator as a kids toy before they had even experienced using one. The second fear was that the simulator was going to provide management with the ultimate tool to weed out the less than desirable employees. It was imperative that the myths were dispelled immediately and drivers were reassured of the intent of the simulator and that any criticism of the simulator was welcome as it would be viewed objectively, investigated thoroughly and if found to be substantiated, used to make adjustments to the simulator as a means of making it as close as possible to the real thing. The tuning process in stage 1, the continued fine tuning process based on the evaluation of driver feedback and the excellent learning program employed were all important factors in establishing driver confidence, support and the overall success of the program. REFERENCES 1. Wilson P., Train Dynamics Learning Concept QR Internal Report. 2. Roach D.E., Wilson M.P., Effective Driver Training Utilises Train Dynamics Research Proceedings 6 th International Heavy Haul Conference. 3. Eichinger M., Geraghty B., Wickham T., Investigation of the Viability of Alternate Cane Train Operations Using Simulation & Trials Proceedings CORE 2004.