This was seen as a good opportunity to improve pulp feed control in the machine. There are sophisticated arrangements to ensure an even supply of stock (consistency about 2% w/w wood fiber in water) from the headbox across the width of the paper machine take-up. Most of the remaining water evaporates and fiber-to-fiber bonds are formed as the paper contacts a series of steam-heated cylinders in the dryer section.
This chapter presents an overview of the origins of paper and details the evolution of the papermaking process over the years. The flow spreader takes the incoming flow of pipe stock and distributes it evenly across the headbox across the width of the paper machine. This shortens the distance needed to remove the stock water and eliminates one side of the paper.
The variations observed on a sheet of paper can be of three types (Figure 2.2): the Machine Direction variations (MD), the Cross Direction variations (CD) and the random variations, which are a composite of the two previous types of variations. Double-sidedness: The two surfaces of a paper vary due to the formation of the paper on a forming wire. Directionality: The general orientation of the fibers in a sheet of paper tends to be in the direction the paper machine is running-machine direction.
The paper machine approach flow refers to the operations that transfer pulp from the machine chest to the paper machine.
PM2 Paper Machine
A cascade control structure has two feedback controllers with the output of the primary (or master) controller changing the setpoint of the secondary (or slave) controller. Another question concerns the robustness of the controller, i.e. the tolerance of the controller to changes in parameters. In the model reference control system, the control signal is generated according to the difference between the output of a model and the output of the actual plant.
Model construction starts from a division of the system to be modeled into two parts: the model and the environment. The purpose of the model plays a major role in determining the shape of the model. In this phase it is ensured that the model is a true representation of the system in question.
The gradient method uses only the first derivatives of the objective function in the calculations. The major problems with the model concerned the excessively long simulation times and the lack of knowledge about the process. Subsequently, Tessier and Qian (1994) modeled the dynamic behavior of the system from the chip silos to.
The process involves several unit operations of the stock preparation area (separation, mixing and dilution) and includes all its controllers (level controller LC, flow controller FC, consistency controller CC and a ratio controller). In the structure of the program, each flow is subjected to the action of a flow controller. The Y-vector collects all the necessary data regarding the mass (m), the consistency (c), the currents (if and a) and their derivative forms (m,c,j,a).
The u-vector gathers the external inputs of the system such as the mass set points (msp), the consistency set points (csp) and the flow set points (sifsp). Every single flow of the system is under the control of a FC (flow controller GCI). It contains all the system information and calculates from the valve positions the 'new' properties (volumes, flows and consistencies).
Most importantly, some degree of fundamentalism has been applied in the creation of the model.
Blend chest
The graphical user interface (GUn
Tuning the controllers is also made much easier due to the scroll buttons on the screen. The main part of the window represents the storage preparation area with all its units. At the bottom, a graph of the object selected by the user (by clicking the appropriate 'radio button') is displayed (eg the graph in Figure 6.1 represents the mixed breast output flow).
This M file provides a framework for the implementation of callbacks (the functions that are executed when users activate components in the GUI). This is especially appreciated when the system's response to a disturbance is to be observed. Tuning of the various controllers was performed according to the trial and error tuning technique (the gain is first set by a step-response analysis, then integral action).
In the algorithm, the form of the objective function is modified for programming purposes (section 5.4). In the optimizer, the total flux is kept as close as possible to the output of the level controller (Fsp). Another addition to the algorithm was the introduction of intermediate consistencies in the objective function formulation (section 5.4.3).
All requirements regarding proportions, mixing box consistency and total flow are met. The order of priority in dealing with restrictions was established in agreement with Mondi. Many approximations were made in the design of the model to simplify the understanding of the system.
It is intended that this work will provide a basis for online implementation of the optimizer in the plantDec. This represents a good basis for further investigation and improvements in the control of the stock preparation area. The proportional action moves the control valve in direct proportion to the size of the error.
The integral action moves the control valve based on the time integral of the error (eq E-2). When the error becomes positive (e > 0) or negative (e < 0), the integral of the error drives the controller's output either up or down, depending on the controller's action (inverse or direct).
