Research On Optimization And Control Of Dredging Operations For Cutter Suction Dredgers

As a class of the most important dredging equipment, Cutter Suction Dredger (CSD) is widely used in dredging projects. Since it can cut off soil and transport it to designated discharging place at one time, it plays an important role in waterway reclamation, port construction and ocean engineering projects. Dredging projects with cutter suction dredgers are characterized by heavy equipment investment, long operation time and high unit cost. Operation optimization and control is one of the most important researching topics in the dredging researching area. Optimized dredging operation can reduce cost and raise dredging efficiency remarkably.The dynamics of dredging projects are very complicated, the characteristics of dredging equipment are continuously changing with working conditions and environment. In this paper a complete solution for dredging automation is presented. The core of the solution has two aspects: An expert system that performs online optimization, fault prevention and control inference; a set of properly designed controllers that work corporately to control the actions and key process parameters, especially controllers for slurry concentration and slurry velocity.Based on operating experiences of dredging workers and also the technical documents of dredging system available, expert system reaches the goal of operation optimization and fault prevention. The structure, knowledge representation and maintenance, and also the inference mechanisms of the expert system are researched. A prototype of the expert system is developed with C++ as proposed. With a properly updated 900 type dredger, a platform for dredging operation optimization and control experiments is constructed. Field experiments are carried to test the optimization performance of the proposed scheme. Results show that the expert system can adapt to the continuous changing working conditions, safety and efficiency of dredging operations are steadily raised; the fluctuation of dredging process is smaller.Multiple aspects are included in the issuse of dredging process control, such as ladder control, trolley position control, slurry concentration control, swing movement control, slurry velocity control. However, design of controllers for the slurry velocity and slurry concentration is the most important. After a brief description for the development of all controllers in the whole system, focus of this paper is placed on the control problems of slurry velocity and slurry concentration. Self-tuning feedforward control schemes are separately developed for both processes, feedforward control is introduced to compensate the interaction of the processes. Through theoretical deduction, simulations and also field experiments, control performance of the proposed schemes are verified and also compared with conventional PID controllers. The chapters of the paper are arranged as following:Chapter 1, the background, targets and significance of dredging operation optimization are introduced. Key researching contents, significances and difficulties are analyzed. Furthermore, current states of dredging process control are also analyzed. In the last section of this chapter, development and application of artificial intelligence, especially expert system, are briefly analyzed.Chapter 2, the constitution and working principle of dredging system is introduced. Model of slurry transportation system is analyzed. The characteristics of mud pump, the pressure loss of pipeline system and also the load characteristics of diesel engine are all analyzed. This chapter provides a necessary basis for further research on dredging system.Chapter 3, the goals, influence factors and currently used optimization strategies are thoroughly analyzed. First, the basic goals for dredging operation optimization are clearly specified; afterwards the difficulties, key influence factors and their complicated cross relationships are analyzed. The principles of currently applied empirical knowledge based optimization methods are introduced in the end.Chapter 4, A scheme for solving the dredging operation optimization problem with the principles and methods of expert system is thoroughly analyzed. The expert system structure proposed has incorporated two inference engines that are working cooperatively to realize operation optimization and fault prevention. The knowledge representation and maintenance methods, the design of inference procedures and also the details of approaching the dynamics of dredging system with neural networks are all analyzed in this chapter.Chapter 5 deals with the process control problem of dredging system. At first, the overall description for dredging process control is presented. The focus is put on the slurry velocity and slurry concentration control problems. Self-tuning feedforward control schemes are designed for both processes. In the proposed scheme for slurry concentration control, a structure with two control loops is proposed; an internal loop is designed to control the swing speed, a self-tuning feedforward controller is placed in the outer loop so as to control the slurry concentration. A zero pole configuration self-tuning feedforward scheme is introduced for slurry velocity control. With computer simulations, it has been proved that the proposed schemes can realize the goal of stable control of slurry concentration and velocity.Chapter 6, experiments are carried out to test the performance of the expert system and the control schemes for slurry concentration and velocity. The hardware and software system of a test platform that is constructed by updating a 900 type dredger are introduced. Performances of the optimization expert system are tested with field experiments. Experiment results show that the system can steadily control dredging efficiency at a comparatively higher level; it can accurately identify the possible fault states and provide appropriate control adjustments. Experiments are also carried out on site to test the slurry concentration and slurry velocity control schemes, Experiment results are analyzed is this chapter.The key achievements and conclusions are summarized in Chapter 7, and the future researching work on this topic is also proposed in this chapter.