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2008 ANNUAL REPORT 19 A. INDUSTRIAL CONTROL AND OPTIMISATION Program Goals: A The partnerships between researchers and industry enable reciprocal transfer of knowledge and new ideas of great potential impact on the community and economy. This program encompasses several research projects motivated by and in collaboration with industrial partners. The main underlying theme of these projects is the application of advanced modelling, control and optimisation techniques to maximise asset utilisation and improve performance. The complexity of the dynamics of such processes arise from factors including model errors, unknown disturbances, nonlinearities, distributed parameter systems, elements of Human-Machine Interaction and hybrid (discrete and continuous state) components. Expected outcomes of the program include high quality research Julio Braslavsky solutions and human resources tailored to the needs of Australian industry. Program Leader In 2008 we welcomed CfW Hamilton Jet & Co (New Zealand) and Boeing Research & Technology, Australia as Industrial Affiliates, joining Halcyon International, CSR, Connell Wagner and Industrial Automation Services (Hatch IAS). A.1 PERfORMANCE OPTIMISATION Of MARINE SYSTEMS Project Leader: T. Perez Researchers: C. Løvaas, G.C. Goodwin and J.C. Agüero External Academic Collaborators: T.I. fossen (Norwegian University of Science and Technology, Norway) C. Holden (Norwegian University of Science and Technology, Norway) E. Herrero, (Student, University of Cantabria, Spain) Tristan Perez Deputy Program Leader External Industrial Collaborators: P. Steinmann (Halcyon International, Australia) T. Armstrong (Austal Ships, Australia) J. Borret (Hamilton Jet, New Zealand) M. Santos-Mujica (Robotiker-Tecnalia, Spain) S. Peder-Berge (Offshore Simulator Centre, Norway) Marine systems are designed to perform complex operations that require appropriate reliability and economy. These requirements demand an interdisciplinary approach to address the tight integration of design aspects related to hydrodynamics, structures, and motion control. This project is dedicated to the design of tools for guidance and motion control with the aim of optimising the performance of marine vehicles in different operations. The project targets vessels and operations within offshore, maritime transport, underwater exploration, unmanned vehicles and wave energy conversion. Some of the current research is being conducted together with international academic and industry collaborators. figure 1: Marine systems are designed to perform complex operations that require appropriate reliability and economy. These requirements demand an interdisciplinary approach to address the tight integration of design aspects related to hydrodynamics, structures, and motion control. (Picture courtesy of Offshore Simulator Centre AS, Norway). 20 ARC Centre of Excellence for Complex Dynamic Systems and Control A A.1.1 Marine system simulation tools Researchers: T. Perez and T. fossen (Norway) The Marine Systems Simulator (MSS) is an environment developed to provide the necessary resources for rapid implementation of mathematical models of marine systems with focus on control system design. The platform adopted for the development of the MSS is Matlab/ Simulink. This allows a modular simulator structure, and the possibility of distributed development. Openness and modularity of software components have been prioritised in the design. This enables a systematic reuse of knowledge and results in efficient tools for research and education. In 2008, we added functions for frequency-domain identification, which are an aid to the construction of time-domain models from frequency-domain data computed from hydrodynamic codes. The latest version of the software and future updates can be freely downloaded from www.marinecontrol.org A.1.2 Identification of radiation force parametric models of marine structures from 2D frequency domain data Researchers: T. Perez and T. fossen (Norway) The ability to predict ship responses and loads in waves is an important tool in the design of marine structures and motion control systems. One method for constructing time-domain models consists of using the data generated by the hydrodynamic codes to compute the different elements of the so called Cummins’ equation of ship motion. In this project, we have been studying the application of different identification methods in both time and frequency domain to make best use of the available hydrodynamic data and constraints based on prior knowledge derived from hydrodynamic theory. In 2008, we addressed the problem of joint identification of infinite-frequency added mass and fluid-memory models of marine structures from finite frequency data. This problem is relevant for cases where the code used to compute the hydrodynamic coefficients of the marine structure does not give the infinite frequency added mass. This case is typical of codes based on 2D-potential theory codes. The method proposed presents a simpler alternative approach to other methods previously presented in the literature, and the same identification procedure can be used to identify the fluid-memory models with or without having access to the infinite-frequency added mass coefficient. Therefore, it provides an extension that puts the two identification problems into the same framework. a) b) 3D Visualisation of the Wamit file: semisubow.gdf Convolution Model DoF 33 x 10 Potential Damping DoF 33 7 150 2.5 140 B 2 Best FD ident, order 8 130 1.5 B [Kg/s] |K(jw)| 120 10 1 110 K(jw) 0 100 K (jw) order 8 0.5 hat Z−axis (m) 90 0 −10 10 −2 10 −1 10 0 10 1 10 −2 10 −1 10 0 1 10 Freq. [rad/s] Freq. [rad/s] x 10 7 Added Mass DoF 33 −20 100 7.5 7 −30 50 Phase K(jw) [deg] 6.5 A [Kg] −20 0 6 0 40 5.5 A 20 20 −50 40 0 Aest FD indet, order 8 5 60 −20 Ainf −40 −100 4.5 −2 −1 0 1 −2 −1 0 1 10 10 10 10 10 10 10 10 Y−axis (m) X−axis (m) Freq. [rad/s] Freq. [rad/s] figure 2: frequency domain identification of marine structure dynamics base on 2D hydrodynamic data. (a) shows the geometry of an offshore rig used as an application example (data from www.marinecontrol.org). (b) shows the identification results for the frequency response of the fluid memory function in heave based on a parametric model of order 8 without using infinite frequency added mass as part of the available data. This figure also shows the reconstruction of added mass and damping from the identified parametric model. 2008 ANNUAL REPORT 21 A.1.3 Modelling and control of parametric resonance in marine vessels Researchers: T. Perez, T. fossen (Norway) and C. Holden (Norway) A Parametric resonance is a phenomenon where changes in the model parameters can be used to describe rapid build-up of oscillations. This phenomenon has been observed in the rolling motion of contemporary ship designs with significant bow flare and raised sterns. The resonance can be developed when sailing in a head and stern seas with a wave length similar to the length of the ship. In these conditions, the hydrodynamic forces that restore the up-right equilibrium of the vessel present a time-varying characteristic, which depends on the location of the wave crest along the length of the vessel. This is equivalent to a mass-spring-damper system where the stiffness of the spring varies with time. This effect results in roll parametric resonance due to changes in the restoring forces, which creates a rapid development of roll motion that can reach up to 40deg in just a few roll cycles. This phenomenon has been responsible for containers being washed overboard in container ships and capsizing fishing vessels. This project examines the modelling of this phenomenon and the use of controlled roll stabilisation devices to reduce the effect on the vessel motion. In 2008, a parametric model previously proposed in the literature has been fitted to experimental data of a scale model of a 300m containership, and a model of the coupled ship and an actively controlled u-tank stabiliser has been developed. a) A.1.4 Experiment design and identification of marine vessels dynamics Researchers: T. Perez, G.C. Goodwin, J.C. Agüero, E. Herrero (Spain) and T. Armstrong (Austal Ships) The mathematical models of marine vessels can be obtained from first principle or analytical modelling. The parameters of the model, however, often need to be estimated from full scale trials. The ability of an estimation method to produce good estimates of the parameters depends on how much information about the dynamics of the system is contained in the data, which in turn depends on the experiment performed. Hence, the design of experiments is of paramount importance to obtain accurate model parameters and to reduce the time to perform experiments. Some of the hydrodynamic phenomena involved in ship dynamic response are too complex for analytical modelling. b) Therefore, parts of the model structure may also be determined based on analysis of experimental data. Trial u Model This project examines the design of optimal experiments and the application of system 0 50 100 150 200 250 300 350 identification or experimental modelling for vessels performing different operations: Estim dynamic positioning, manoeuvring at low speed and manoeuvring at high speed. Model v 0 50 100 150 200 250 300 350 In 2008, we focused on a two-stage approach for experiment design, in which we first Time [s] collect data from step responses, and then based on the information gathered, appropriate Trial signals for parametric model identification are designed. This method has been applied p Model 0 50 100 150 200 250 300 350 to the identification of vessels for positioning and slow speed manoeuvring using data of full scale trials of a small fishing vessel. In addition, we have proposed the application Trial of statistical methods to select hydrodynamic damping model structures that provides r Model 0 50 100 150 200 250 300 350 best use of the information available. These methods have been applied to simplify the Time [s] manoeuvring model of a 130m a novel fast ferry trimaran designed and built by Austal Ships, Australia. figure 3: Time-domain identification of a coupled 4DOf manoeuvring model of a highspeed vessel. (a) shows Austal’s High-Speed Trimaran Design (Picture courtesy of Austal Ships, Australia). (b) shows the identification results of a manoeuvring parametric model with structure selected via stepwise regression in the time domain. The figure shows the model and fullscaletrial responses in velocity: surge (u), sway (v), roll (p), and yaw (r). 22 ARC Centre of Excellence for Complex Dynamic Systems and Control A A.1.5 System identification for rapid model prototyping of ship training simulators (Offshore Simulator Centre AS, Norway.) Researchers: T. Perez and S. Peder-Berge (Norway) Ship training simulators are used to improve crew efficiency and thus safety of marine operations. At the core of any virtual-reality simulator lays a mathematical model that describes the ship dynamic response to control and environmental forces. When a new vessel is to be incorporated into a simulator, different type of data for the vessel may be available to be used for system identification to extract a mathematical model for dynamic response. In 2008, CDSC evaluated various identification methods for obtaining parametric models of vessel response based on frequency-domain data computed by hydrodynamic codes, and made recommendations to the Offshore Simulator Centre AS as to which method to use for rapid identification of vessel dynamic response. figure 4: Ship training simulators are used to improve crew efficiency and thus safety of marine operations. At the core of any virtual-reality simulator lays a mathematical model that describes the ship dynamic response to control and environmental forces. When a new vessel is to be incorporated into a simulator, different type of data of the vessel may be available to be used for system identification to extract a mathematical model for dynamic response. 2008 ANNUAL REPORT 23 A.1.6 Adaptive control of gyroscopes for roll stabilisation of marine vessels (Halcyon International, Australia) reliable computer control systems ensure adequate operation over an envelope of sailing conditions. This project has examined methods for A tool, a designer is able to have a fast initial assessment of the design before refining the performance predictions using the time-domain simulation code GYROSIM. Researchers: T. Perez and determining the size of the gyros to achieve Also in 2008, an adaptive control strategy P. Steinmann (Halcyon) a desired level of roll reduction and also was designed to maximize performance The use of gyroscopic effects of high for control design to ensure performance of the gyrostabiliser in changing speed spinning masses for the roll in a range of sailing conditions. A time- environmental conditions. This strategy stabilisation of marine structures was domain simulation tool GYROSIM has been aims at dealing with a fundamental trade- proposed over 100 years ago. This developed, which allows a rapid evaluation off in gyrostabiliser control design in which approach was very effective, but limited of the expected performance high performance in roll reduction must be control and large sizes hindered further of gyrostabilisers. balanced with potential constraint violations developments. In recent years, there has when a large group of waves arrive at the In 2008, a frequency-domain simulation been significant interest in revitalising vessel. The results have been tested in tool GYRODESIGN has been developed for gyrostabilisers due to improvements in simulation and an experimental prototype a rapid gyro sizing and to provide design materials, bearings, and lubricants, which is being built for further testing in real information related to mechanical and have contributed to fast spinning devices sea conditions. hydraulic component specification. With this and size reduction. In addition, fast and figure 5: Halcyon’s twin gyrostabiliser. A gyrostabiliser consists of one or more spinning wheels whose gyroscopic-induced forces are used to counteract the forces induced by the waves on a ship, and thus reduce motion. In a roll gyrostabiliser, the spinning wheel is positioned such that the gyroscopic effect reduced the motion in roll. The use of twin wheels rotating in opposite directions eliminates the gyroscopic effects in other degrees of freedom than the one intended to be controlled. figure 6: Numerical study of roll gyrostabilisation of a navy patrol boat using Haylcyon’s gyro stabilisers. The top plot shows the roll angle at zero forward speed in a 3m sea state as a function of the wave period. The bottom plot shows the expected percentage of roll angle reduction (RMS). 24 ARC Centre of Excellence for Complex Dynamic Systems and Control A A.1.7 Control design for optimum power extraction in wave energy converters (Robotiker-Tecnalia, Spain) Researchers: T. Perez and M. Santos-Mujica (Spain) The search for renewable energy resources has revitalised the interest in devices for wave energy conversion. Wave energy converters (WEC) extract energy from the motion induced by the waves on particular hull designs. In order to maximize the extracted energy, the design of a control below to regulate the loads of the power take off element is of great importance. In this project, we look at the control aspects of WEC. In particular, we are studying fundamental performance limitations that can affect potential control system designs for maximum energy extraction. The preliminary results are exciting and we thus plan to submit an ARC Discovery Project dedicated to this topic in 2009. In 2008, we have analysed a particular WEC device, which uses a large gyroscope mounted on a floating platform. This device extracts energy from precession motion induced on the gyroscope as a result of the pitching motion of the structure. Based on a model obtained from a combination of hydrodynamic computations and experimental tests, we have computed an upper bound on the expected power to be extracted in a various sea states. We also have guided ROBOTIKER in the development of a time-domain simulation package, and designed an adaptive precession torque control that aims at optimising the extracted power. a) b) Power Extraction−Head Seas 350 Ideal PTO Gyro Bg = 15, 35, 65 x sqrt(4 Ig Cg) 300 figure 7: Performance analysis of a wave energy conversion device with different control designs. 250 figure (a) shows an schematic of a particular wave energy converter that uses a power take off element Pa/ζ2 [kW/m2] 200 (PTO) to extract energy from the wave-induced pitch motion. figure (b) shows the estimated power 150 extraction per unit of wave amplitude as a function of the wave frequency. The ideal PTO is an upper bound 100 on performance estimated using only hydrodynamic characteristics. The use of different control designs 50 and a particular PTO was used to evaluate the potential gains of control adaptation to changes 0 in the dominant wave frequency. W A.1.8 Constrained predictive control of ship fin stabilizers to prevent dynamic stall Researchers: T. Perez and G.C. Goodwin In moderate to high sea states, the effectiveness of ship fin stabilisers can severely deteriorate due to nonlinear effects arising from unsteady hydrodynamic characteristics of the fins: dynamic stall. These nonlinear effects take the form of a hysteresis, and they become very significant when the effective angle of attack of the fins exceeds a certain threshold angle. Dynamic stall can result in a complete loss of control action depending on how much the fins exceed the threshold angle. When this is detected, it is common to reduce the gain of the controller that commands the fins. This approach is cautious and tends to reduce performance when the conditions leading to dynamic stall disappear. An alternative approach for preventing the effects while keeping high performance consists of estimating the effective angle of attack and set a conservative constraint on it as part of the control objectives. In our work, we have investigated the latter approach, and here proposed the use of a model predictive control (MPC) to prevent the development of these nonlinear effects by considering constraints on both the mechanical angle of the fins and the effective angle of attack. 2008 ANNUAL REPORT 25 A.2 OPTIMISATION BASED OPERATOR GUIDANCE SCHEMES Project Leader: J.H. Braslavsky Researchers: A G.J. Adams, J.-C. Agüero G.C. Goodwin, B. Godoy (Student), and A.J. Rojas External Academic Collaborators: J.T. Gravdahl (Norwegian University of Science and Technology, Norway), D. Ugryumova (Student, University of Twente, The Netherlands) External Industrial Collaborators: D. Boggs (BHP-Billiton, Perth, Australia) M. Downey (BHP-Billiton, Newcastle, Australia) J. Lee (BHP-Billiton, Newcastle, Australia) A. Maddever (BHP-Billiton, Perth, Australia) R. Turner (BHP-Billiton, Newcastle, Australia) This project is funded by a partnership of the Centre with BHP Billiton, and deals with the development of new technologies using mathematical modelling, and state-of-the-art model-based control and estimation tools. The project currently encompasses three sub-projects: n Modelling and control of copper heap bioleaching processes, n Sferics reduction in electromagnetic mineral exploration, n Cogeneration at WestVAMP A.2.1 Modelling and control of copper heap bioleaching processes Researchers: J.H. Braslavsky and B. Godoy This sub-project focuses on the development of mathematical models and control strategies for heap bioleaching processes for the extraction of copper from sulphide minerals. Heap bioleaching is of increasing interest in the mining industry to recover metals from secondary ores. See CDSC Annual Reports 2003-2007 for more background information. In previous work, we have suggested the use of feedback control to improve the rate of mineral extraction based on linearized models around nominal trajectories of the output of interest. In 2008, this work has been refined in a comparative study between two feedback approaches: Model Predictive Control (MPC) and Extremum Seeking Control (ESC). Previously obtained linearized models were used to design an MPC strategy incorporating input constraints. ESC was tuned, without requiring a process model, to maximise copper extraction rate using aeration rate. Simulation results show that similar copper extraction rates can be obtained using either strategy. The extraction rates improvements with respect to an optimised fixed set-point strategy are between 4-5%, which correspond to 840-1,050 extra tonnes a year for a small bioleaching facility. Both feedback strategies improve robustness with respect to model uncertainties. A paper communicating these results has been presented in the 17th IfAC World Congress in Seoul, South Korea. This project has been the topic of research of Boris Godoy for his PhD thesis, which was awarded in October 2008. 26 ARC Centre of Excellence for Complex Dynamic Systems and Control A Nominal setpoints u0 BHPB Model Estimated nominal outputs y0 Manipulated Process setpoints uk yk Manipulated Process setpoints HPF + u y + − + −γ + LPF × z− 1 ∆u Linear ∆y Sustained MPC excitation A sin(ω k ) figure 8: MPC application to heap bioleaching. A figure 9: ESC feedback strategy schematics. high complexity model is used to estimate linearised Adaptation is applied on the aeration rate setpoint models around nominal trajectories. MPC is tuned based on measurements of copper concentration is to track increments in heap average temperature by leached solution. manipulating aeration rate and raffinate influx. In a real implementation the model could be retuned adaptively. figure 10: Comparison of extraction rates improvements for MPC and ESC with respect to an optimised fixed setpoint strategy. MPC requires less control efforts, while ESC presents slightly faster recovery. 2008 ANNUAL REPORT 27 A.2.2 Sferics reduction in electromagnetic mineral exploration Researchers: J.-C. Agüero, In 2007, broadband noise reduction was achieved using separate models for the low and high frequency ranges. Progress in 2008 has included the development of A those obtained using alternative methods, with similar performance on experimental data. The results indicate that the technique can be successfully applied in this case J.H. Braslavsky, M. Downey, a single, unbiased model for broadband and shows potential for other applications. G.C. Goodwin, K. Lau, (4 Hz-1 kHz) multinode noise cancellation. See Lau, Braslavsky, Aguero and Goodwin J.B. Lee, A. Maddever, A Matlab implementation of the model (2008) in Conference Papers. P. Turner and estimation and noise cancellation future work is planned for the extension of D. Ugryumova algorithms has also been written for our noise cancellation techniques to other BHP-Billiton. Other work includes the This is a joint project with BHP-Billiton types of magnetic field sensors currently estimation of a model for new sensors. Exploration and Mining in Perth being tested by BHP-Billiton. This model can be used for compensation (previously in Newcastle). of the sensor response. The aim of this industry project is the In March, Diana Ugryumova, a Masters A.2.3 Co-generation at WestVAMP reduction of sferics noise in mineral Student from the University of Twente in Researchers: G.J. Adams, G.C. Goodwin, exploration using Geoferret, an Australian The Netherlands, visited for six months to J.T. Gravdahl (Norway) designed and developed electromagnetic complete the practical component of her and A.J. Rojas exploration system. The exploration course. During her visit she worked on the technique employed by Geoferret relies This project is aimed at improvements application of our modelling techniques on the induction of currents in the earth to the control for the Westcliff Vent Air to impedance estimation in another followed by the measurement of the Methane Project facility near Appin, NSW. electromagnetic exploration method: magnetic field generated by the induced The details are subject to a confidentiality magnetotelluric sounding. currents. The reduction of sferics noise, agreement, but overall the facility aims to electromagnetic noise originating In July, a paper on the application of generate power from the methane that is from lightning storms, is central to the errors-in-variables techniques to sferics present in air vented from underground improvement of signal to noise ratio for attenuation was presented at the IfAC coal mines. Work in 2008 focussed on the detection of deeper ore bodies. See World Congress 2008 in Seoul, Korea. deriving a model of the process, and CDSC Annual Report 2006 for more The technique is used to estimate a model control improvements are being background information. which is deployed for noise cancellation. investigated using this model. The estimated model agrees well with Noise cancellation results − PSD Noise cancellation results − Coh ZX 0.4 −7 10 Z residual 10 −8 0.3 −9 0.2 10 −10 0.1 PSD (V /Hz) 10 2 0 −11 0 1 2 3 4 10 10 10 10 10 10 −12 Coh ZY 10 0.8 Z 10 −13 0.6 residual 10 −14 0.4 0 1 2 3 4 10 10 10 10 10 f (Hz) 0.2 0 0 1 2 3 4 10 10 10 10 10 f (Hz) figure 11: Noise cancellation results. Power spectral density of figure 12: Noise cancellation results. Coherence between the Z and X the measured sferics noise Z and the residual after performing components and the Z and Y components of the sferics noise before and noise cancellation. The 50 Hz powerline harmonics have not after performing noise cancellation. The coherence has been reduced to a been removed. negligible level at most frequencies between 4 Hz and 1 kHz. This indicates that almost all of the correlated noise has been eliminated. 28 ARC Centre of Excellence for Complex Dynamic Systems and Control A A.3 INTEGRATED MINE PLANNING (BHP BILLITON) Project Leader: T. Perez uses the estimates of one stage to initialise the subsequent stage. This algorithm is, thus, self-initialised, and the resulting estimates also give the option of initialising Researchers: G.C. Goodwin, M. fu, the first proposed algorithm for a further M.M. Zhang, refinement of the parameter values. figure B. Godoy (Student), 13 shows a particular realisation of a X. Tai (Student) simulated metal price and future contracts. figure 14 shows the estimates of the main External Academic Collaborators: parameters of the model corresponding to K. Barbosa (National Laboratory for 100 different realisations of simulated data Scientific Computing, Rio de Janeiro, Brazil) using the self-initialising algorithm. External Industrial Collaborators: The resulting price models are a critical P.M. Stone (BHP-Billiton) input into BHP Billiton’s models for M. Menabde (BHP-Billiton) understanding the full value of resource The integrated mine planning project assets and for recognizing the value of aims at developing tools for optimisation future embedded management options. of planning and operation of mines. In The latter are a direct outcome of the 2008, CDSC worked on three particular level of equilibrium price uncertainty. In aspects of the problem. The first aspect an ongoing research robust methods for is parameter estimation of models for extending the parameter identification to a commodity prices. The second aspect multi-commodity case are being researched. concerns the development of planning tools The second aspect of mine planning looked and algorithms to optimise both strategic into in 2008 was mining phase design, mine plans and infrastructure investment which is a critical step in long-term mine decisions. The third aspect is the optimal planning process and heavily affects the dispatching scheduling of truck/trains net present value of a life-of-mine plan. In within a mine. BHP Billiton’s proprietary mine planning The major focus of commodity price model tool, the mine planning process are divided estimation was to develop algorithms for into a few sub processes which are parameter estimation. Commodity price optimised separately. The mining phases modelling is normally approached in are developed from an “optimal” block terms of structural time-series models, in extraction sequence from a prior “block which the different components (states) aggregates” scheduling using a fuzzy have a clear financial interpretation. In clustering algorithm. The mining phases particular, we looked at the parameter and developed in this way sometimes are not state estimation of two-factor models. practical, and manual post-processing is These models use a trend towards an required to ensure practical for mining equilibrium price (long term component) operation. furthermore, the existing mining and a reversion to such a trend (short phase design method does not optimise term component), which represents the the net present value. difference between the current commodity To address these issues, we have developed price and the equilibrium price. a heuristic automatic method to enforce the We have developed two different practical mining access constraints. We have algorithms based on the Maximum- embedded the method into a framework likelihood approach. The first algorithm of meta-heuristic optimisation. Testing has uses a full parameterisation, and thus demonstrated that the new approach can results in a non-linear parameter improve the net present value of existing optimisation problem. This algorithm phase designs by the order of 5%. provides high quality estimates provided Also some preliminary investigation has that a good initial guess of the value of been carried out on the problem of optimal the parameters is available. The second train/truck dispatch using approximate algorithm solves a sequence of estimation dynamic programming and discrete event problems that increase in complexity and dynamic simulation. 2008 ANNUAL REPORT 29 figure 13: Sample realisation of a commodity spot and future contract prices based on simulated data. The data is generated using a model with parameters estimated from market data of the 3 Simulated Data A London Metal Exchange. Spot Price 2.5 Spot 1 month 2 5 months 9 months 0 50 100 150 200 months 13 250 Time [Weeks] 17 months 24 months 1.1 1 Log Spot Price 0.9 0.8 0.7 0 50 100 150 200 250 Time [Weeks] Intitial Estimates Usign Spot & Future Prices figure 14: Prediction error parameter estimation 20 results over 100 simulated realizations of commodity prices and future contracts. This figure shows the 18 Kappa estimates and true values of the main parameters of 16 a 2-factor commodity price model. The parameters are the strength of mean reversion (Kappa), trend 14 (mu), and the short and long term initial conditions of the states of the model. The analysis based on 12 simulated data is used to evaluate the properties of 0 10 20 30 40 50 60 70 80 90 100 the estimation algorithm; that is, it is used to gain Realization confidence in the results with real data. 0.2 0.1 ξ 0 −0.1 0 10 20 30 40 50 60 70 80 90 100 Realization 0.24 0.22 0.2 0 χ 0.18 0.16 0 10 20 30 40 50 60 70 80 90 100 Realization 0.72 0.7 0 ξ 0.68 0 10 20 30 40 50 60 70 80 90 100 Realization 30 ARC Centre of Excellence for Complex Dynamic Systems and Control A A.4 NEXT GENERATION MODEL BASED CONTROL TOOLS (MATRIKON) Progress in 2008 occurred in the following areas: n Tools for economic optimisation, including data reconciliation strategies future work on next-generation model- based control tools for CPO will investigate: n Derivation of MPC tuning parameters from parameters of existing (non-MPC) Project Leader: G.J. Adams and interfaces for steady-state control. Researchers: N. Germyn (Student), optimisation of linear and non-linear G.C. Goodwin, A.M. n Robust MPC, based on the algorithm objectives, were developed and Medioli, R.H. Middleton, of Løvaas, Seron and Goodwin (see integrated into CPO (this was completed M.M. Seron and Journal Publication) as an honours student project by Nic J.S. Welsh Germyn). n Testing of the CPOmpc scheduling tool External Academic Collaborators: applied to non-linear processes. n Code changes in the CPOmpc tool D. francois (Université Catholique have resulted in major execution speed n Extending and integrating the economic de Louvain, Belgium) improvements being attained, and optimisation features as part of a External Industrial Collaborators: setup modifications allow on-line model “dynamic process optimisation” loop; P. farragher (Matrikon) changes to occur smoothly. some extensions include strategies R. Thomas (Matrikon) for extremal seeking, and checks for n Handling of non-linear processes via the infeasible setpoints. appropriate scheduling of multiple linear A.4.1 Next generation model based controllers was made possible by the n A major case study is planned for 2009, control tools for CPO development of an MPC Scheduler. which will bring together the features of Researchers: G.J. Adams, N. Germyn, non-linear multivariable control, system n Matrikon are developing a CPOmpc G.C. Goodwin, A.M. Medioli, identification and economic optimisation control solution platform with an R.H. Middleton, M.M. Seron, into the control and optimisation of a overseas company, which will involve R. Thomas and J.S. Welsh simulated nutating grinding mill. the control of non-linear systems via The aim of this project is to deliver to multiple linear regions. Matrikon process control tools that allow n Decoupling strategies that are intrinsic n appropriate handling of complex, to the internal QP objective function nonlinear and heterogeneous processes; of the CPOmpc tool were integrated and tested. Some results are shown in n robust and easy-to-use system figure 15 and figure 16. identification; and n economic optimisation of process variables. 1.5 0.4 u1 (original) Output amplitude u1 (decoupled) Input amplitude 1 0.2 0.5 r1 0 y1 (original) y1 (decoupled) 0 −0.2 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 1.5 0.2 u2 (original) Output amplitude 1 0.1 u2 (decoupled) Input amplitude 0.5 0 r2 0 y2 (original) −0.1 y2 (decoupled) −0.5 −0.2 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 time (sec) time (sec) figure 15: Coupled (green) and decoupled (blue) setpoint figure 16: The associated MV moves in the presence of change responses in the presence of MV constraints. MV constraints. 2008 ANNUAL REPORT 31 A.4.2 Next generation model based control tools for ProcessMORE Researchers: P. farragher, D. francois (Belgium) and A.M. Medioli This project involves the development of algorithms for automatically generating downtime A causes from alarm sets. CDSC work in 2008 involved: n A thorough investigation of alarm data in relation to physical plant layout and components. n A study of possible or expected classification performance, investigating issues such as new alarms and downtimes being added over time, irrelevant alarms etc. n A visit by Damien francois (Belgium), who developed a simple classifier in 2007. n Modification and enhancement of Damien’s classifier, and its application to new data. n Moving towards a system that may be used on-line, to adapt to changing alarm sets and plant configuration. The performance of the original “batch” and static classifier and the latest classifier is summarised in figure 17, where the “Adaptive Centroid” method would be most useful in a real (on-line) system; results show that classification performance for the Adaptive Centroid method, when the system suggests up to seven possible causes, has over 50% accuracy. 100 Batch Centroid 90 Adaptive Centroid Static Centroid 80 Correct Classification Percentage 70 60 50 40 30 20 10 figure 17: Comparison of classifier performance for different centroid generation methods. 0 1 2 3 4 5 6 7 Number of Predictions Items to work on in 2009 include: n Strategies for culling irrelevant data. n Applying the system to other alarm data. n Different handling of data pre and post-downtime. n Better algorithms to try to improve performance. 32 ARC Centre of Excellence for Complex Dynamic Systems and Control A A.5 CSR SUGAR (INDUSTRIAL AffILIATE) Project Leader: G.J. Adams Researchers: B.J. Burke (Student, CSR Sugar), G.C. Goodwin, A. Rayner (Student), A.J. Rojas and B. Sims External Academic Collaborator: J.T. Gravdahl (Norwegian University of Science and Technology, Norway) External Industrial Collaborator: R.D. Peirce (CSR Sugar) A.5.1 Constrained, multi-variable control of an integrated sugar mill system for economic enhancement Researchers: G. Adams, B. Burke, G. Goodwin, J. Gravdahl (Norway), R. Peirce, A. Rojas In today’s carbon – and energy-conscious economy, manufacturers are searching for ways to maximise returns and minimise wastage. This is certainly true in CSR Sugar, where at Pioneer Mill (near Ayr, North Queensland) a co-generation plant has been installed. This plant uses waste cane fibre (bagasse) from a number of mills to create steam for both sugar milling and for electricity generation. If steam is used for sugar milling efficiently, CSR can export 50MW of power to the local grid, gaining an income stream from a waste product. This project aims to study energy and steam use in sugar processing at Pioneer. The multi-effect evaporators are core units in the process, where the most efficient evaporation of water from syrup occurs. Proper control of the sugar content (brix) coming out of the evaporators, and coordination of steam/energy use with other sections of the mill, are essential to minimise energy losses. The current control of the brix suffers from periodic disturbances (90 minute periods), as well as quicker oscillatory disturbances (10 minute period) caused by addition of water to the syrup upstream during mill stoppages. CSR and CDSC have studied evaporator operation, and have determined the cause of the quicker oscillation to be a type of non-linear flow reduction through the outlet valves. With strategies in place to reduce this non-linear flow effect in evaporator “5A”, oscillations are reduced from those seen in figure 18 to those in figure 19. Evaporator “5B” still suffers from these oscillations in figure 19. Investigation continues into the cause of the slower (90 minute) oscillations. These oscillations are most likely to be due to the steam demand from the batch crystallization pans. Signal analysis is being done on selected process variables to uncover significant correlations. 2008 ANNUAL REPORT 33 A.5.2 CSR brake van control Researchers: A. Rayner and B. Sims A Sugar cane trains use a wagon at the end of the train, called a brake van, to control train braking. The main aim of the brake van is to keep the couplings between each of the cane bins in tension. Once the couplings go into compression, derailments can occur, especially when the bins are empty. The operation of the brake van is via radio link from the locomotive, and consists of a numbered dial with increasing amounts of brake pressure. A park brake can also be independently applied. CSR is looking to improve the control of the brake van with an automated process (i.e. the brake van automatically selecting the appropriate level of braking for the conditions), as well as implementing a new braking unit. In making these improvements, CSR is looking to increase the efficiency of the whole process of transporting cane. By automating the braking system they will effectively save money on driver training, reduce fuel needs (since the brakes are being used more effectively) and cut down on the number of replacements to the brake pads. Replacing the current brake type with an electrical braking system will save further, since electrical systems brake much faster than the current system. Work performed by CDSC on this project so far involves initial modelling of the forces involved in the carriages/couplings. further work will concentrate on this modelling aspect, and it is envisaged that systems which incorporate GPS information as well may be useful. figure 18: Examples of poor brix control behaviour in figure 19: Improved brix control in 5A (red) compared with 5B (blue). evaporators 5A (red) and 5B (blue). 34 ARC Centre of Excellence for Complex Dynamic Systems and Control A A.6 CONNELL WAGNER (INDUSTRIAL AffILIATE) Project Leader: J.S. Welsh A.7 HATCH (INDUSTRIAL AUTOMATION CONTROL) (INDUSTRIAL AffILIATE) A.8 BOEING RESEARCH AND TECHNOLOGY, AUSTRALIA (INDUSTRIAL AffILIATE) Project Leader: J.S. Welsh Researchers: D. Allingham and Project Leader: G.C. Goodwin J.S. Welsh Researchers: T. Perez and J.S. Welsh Researchers: A. Rojas, C. Renton External Industrial Collaborators: External Industrial Collaborators: (Student), G.C. Goodwin J. Tusek (Connell Wagner) B.P. Williams (Boeing Research and External Industrial Collaborators: Technology, Australia) This project, being undertaken in T. Domanti (Hatch IAS) V. Wheway (Boeing Research and conjunction with Connell Wagner, is G. Wallace (Hatch IAS) Technology, Australia) investigating methods of parameter estimation for synchronous machines. In 2008, the work focussed on cross This project began in December 2008. directional control issues in galvanizing The details of the project are confidential, Its aim is to provide validation for lines. The project had two streams: but involve the development of flight methods already in use, to explore systems for autonomous aircraft. alternative approaches and to investigate (i) Rapid estimation of coating thickness ways of providing error estimates for under non-stationary conditions. Here parameter values. it was found that it was desirable to use an estimator having variable memory. (A Synchronous machines are the primary conference paper has been written based generators of electricity for the power on this work). production industry. Estimation of machine parameters is a vital field of study, with (ii) Combining strip location with double literature dating back to the 1920s. Many sided measurements to give improved approaches are available, using different thickness estimation. measurement and model regimes. Most The latter was principally the work of current practices are based upon recent C. Renton who did this as part of his final IEEE standards (for example, IEEE Std year honours project. 115-1995 and IEEE Std 1110-2002) which describe in detail both standstill and on-line tests for synchronous machines. Our work to date has focussed on standstill ra ll l fkd1 l fkd2 l fd rfd frequency response (SSfR) modelling. Id Ifd Here, sinusoidal inputs over a range of −1 Zdd 0 Zfd −1 Zdd0 −1 pGfd l kd1 l kd 2 10 10 10 10 frequencies, approximately from 0.5 mHz to −1 10 Vd Vfd 1 kHz, are applied to the machine in a variety Lad −2 −2 −2 −2 Gain 10 10 10 10 of configurations, and the machine response rkd1 rkd 2 −3 10 is measured. Using these responses, 10 −3 −4 10 −3 10 −3 10 90 90 90 90 transfer functions are calculated and the 60 parameters of an equivalent circuit model figure 20 60 60 60 30 Phase−shift 0 of the machine, shown in the accompanying 30 30 30 −30 figure, are then estimated. The measured −60 0 0 0 −90 responses along with fits obtained from two 10 −4 −2 10 0 10 2 10 10 −4 −2 10 0 10 2 10 10 −4 −2 10 10 0 2 10 10 −4 −2 10 10 0 2 10 estimation methods are also shown. Zdd Zfd Zdd0 pGfd Zff Zdf Zff0 pHdf −1 0 −1 −1 1 −1 1 1 10 10 10 10 10 10 10 10 The parameters from the SSfR tests will, −1 10 0 −2 0 10 10 10 in turn, be used to estimate the machine 0 Gain −2 −2 −2 −2 Gain 10 10 10 10 10 response for on-line step tests, which involve −3 10 10 −1 −3 10 −1 10 small changes to the amplitude of the 10 −3 −4 10 −3 10 10 −3 10 −2 −4 10 −2 10 −1 10 90 90 90 90 90 90 90 90 machine’s power when it is under load (for 60 60 example, when it is connected to the power 60 60 60 30 60 60 60 30 Phase−shift Phase−shift 0 0 grid). from these tests, time constants are 30 30 30 −30 30 30 30 −30 estimated which describe the machine’s −60 −60 0 0 0 −90 0 0 0 −90 response to events such as faults, and how 10 −4 −2 10 0 10 2 10 10 −4 −2 10 0 10 2 10 10 −4 −2 10 10 0 10 2 10 −4 10 −2 10 0 2 10 10 −4 −2 10 0 10 2 10 10 −4 −2 10 0 10 2 10 10 −4 −2 10 10 0 2 10 10 −4 −2 10 10 0 2 10 fast it can recover from such events. 21: figure Zff Plots of transfer function estimates vs. real data for a large synchronous machine. Zdf Zff0 pHdf 1 −1 1 1 10 10 10 10 0 −2 0 10 10 10 0 Gain 10 −1 −3 −1 10 10 10 −2 −4 −2 −1 10 10 10 10 90 90 90 90 60 60 60 60 30 Phase−shift 0 30 30 30 −30 −60

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