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Models of variable parameter flow processes are derived and their control is discussed. Using a pH reactor with varying flow rate as an example a variable transformation is introduced, which makes the model equations invariant under a new time scale. For a restricted class of flow processes classical analysis and synthesis methods can then be used to design a proper controller to guarantee the system stability and performance in general. The method is illustrated by developing a time-varying PID-controller algorithm as an example. Both simulation results and pilot-plant tests are reported, to verify the performance of the controller.

Two methods for instrumental measurement of the dry line have been developed. They are based on innovative arrangements of illumination and related observation under which the pulp on wire is seen by video camera as a composition of dark and light fields. The complete dry line is extracted from the image and used industrially for feedback control of the dry line in machine direction (MD). Recorded correlations between changes of dry line and dry basis weight profile justify conclusions on their control in cross direction (CD) and on incorporation of dry line measurement into conventional MD and CD controls which are handicapped by a long process delay.

Dynamic models of pulps in industrial flotation cells are presented with emphasis on the modelling of their chemical conditioning. They are reduced to steady state models that can be combined to describe banks and networks of cells. Kinetic characterization of minerals in the plant is outlined on the basis of samples from appropriate points, their analysis and simulation between such points, in order to model the solids transfer from pulp to froth in the cells. Models of the froth layers and studies of their surface views are reviewed. Images of froths have been recorded in two plants and results of their analysis are presented. Scene analysis is considered a valuable means in acquisition of new information of processes in flotation cells.

Inspired by a recent review article on the Automation of the Mining, Mineral, and Metal Processing Industry (MMM), this paper will discuss some important ”patches” of computer aided measurement processes in the MMM processing industries. Both evaluation of measurement data, as well as real-time control of the measurement processes will be covered. Notes will be given on the use of machine vision, electrochemical potential, optical emission spectrometry, and X-ray spectrometry. Some visions for the future will be offered.

Grinding systems are often modelled by multivariable impulse response or weighting function matrices. These kind of models are very suitable for design of model predictive controllers. Two predictive control algorithms have been developed and tested with two simulated models of grinding systems. Their design is based on decoupling of the steady state interactions. The dynamic interactions cannot be eliminated but their strengths are reduced by dynamic compensation.

An approach using Petri nets to modelling and control of flexible manufacturing systems (FMS) is worked out in the paper. A class of Petri nets suitable for the modelling of FMS and for the design of their control is formulated. In the Petri net models not only conflicts due to mutual exclusions of the shared system resources but conflicts of branching operations are analyzed and solved, as well. Models of FMS suitable for system performance analysis and control design are built up of a set of elementary building blocks proposed in the paper. Analysis, modelling method and control design is developed on the background of a pilot experimental FMS developed at the Helsinki University of Technology.

Industrial processes are usually controlled by means of controllers with constant tuning parameters. Such controllers pay no attention to disturbances or changes in process variables. The paper analyses such processes and models and derives time-varying controllers, which automatically remove effects caused by the time-varying parameters of the process. The algorithms have been tested in a pilot plant using a commercial control software implemented in PC. The test results are described and the performance of the controllers is demonstrated and compared to theoretical results.

The kinetics of industrial flotation are studied with special reference to grain size, chemical conditioning and liberation. Related models are derived for batch flotation in the laboratory and for continuous flow flotation in a plant. For practical testing of their relationships, specimens of regular mineral and samples from the output of an industrial conditioner are floated in a laboratory cell and radioactively labelled specimens of the same mineral in a plant cell. Timed samples of concentrates and tailings are collected and classified, and kinetic coefficients are evaluated in each case for different grain size and conditioning. Use of these data in theoretical models provides consistent results. The quantitative relationships of the kinetics in laboratory and plant delivered by the method have a central role in the scale-up of flotation from the laboratory to a plant cell, including that of the effects of various control inputs. The dynamic flotation models obtained are reduced to steady state and can then be used for model construction and control design of larger flotation plants.

Typical structure of flexible manufacturing cells (FMC) used in industry is described and reported projects for analysis and development of their controls are reviewed. The paper is limited to studies involving physical cell models for implementation and testing of control algorithms in real time and benefits of the experimental approach are discussed. Control methods implemented by various research groups are event-based or apply expert system, Petri net model or dynamic programming approach. Some pilot cells use intelligent sensors in order to adapt to random features of objects.

The organizer and moderator of the discussion, Professor B.G. Tamm opened the Session with a short introduction and comments on the paper prepared for this discussion and published here. Then the word was passed to panelists and participants from the floor. The following is a summary of the comments made by the participants.

Dynamic model of the rotary grinding mill with variable material flow is a superposition of the kinetic grinding process and the flow pattern characterized by the residence time distribution. A dimensionless residence variable is introduced by integrating the slurry flow through the mill and the time varying model is brought to invariant form by application of this variable. A recursive on-line identification method is developed for open and closed grinding loops by parametrizing the residence distribution with a set of unknown amplitude readings. With Extended Kalman Filter, both the kinetics and the residence distribution are estimated under variable process flow.

Models of continuous flow vessels with variable volume and flow are studied and methods are developed for their recursive identification. New, composite variables have been introduced recently and it has been proven that many time-variable flow systems have only constant parameters, if they are modelled as functions of such variables, assuming their flow patterns are invariant under changes of flow and volume. The recursive prediction error algorithm is now first formulated in terms of the variable which matches to the case of constant volume and variable flow, and the process is identified by estimating a set of 20 successive, equidistant values of the weighting function. Similar algorithms are then written and identifications executed in the cases of variable volume and flow. The study is extended to the case of a flow pattern which changes independently of the simultaneous variation of the total flow and volume, and the parameters of the pattern are tracked. The identifications of the simulated processes prove the superiority of the approach with regard to algorithms written in terms of the regular time variable. Extension of the method to estimation of chemical reaction parameters is discussed against this background.

Models of continuous flow vessels with variable volume and flow are studied and their control algorithms developed. It is shown that the weighting functions of many vessels which are time-variable under such conditions, are invariant with regard to new, composite variables which are introduced. The validity of the basic assumption on invariability of the flow pattern under flow and volume variations is shown valid for several, analytically known cases. The approach is extended to arbitrary, non-analytic response functions obtained by measurements. Automatic controllers are then introduced for processes with variable liquid volume and flow. Both the feedforward and feedback controllers developed in terms of the new variables have constant parameters and eliminate correctly the effects of the disturbance variables in control of concentration or other material property. The corresponding gain scheduling controllers are also presented which operate in terms of the time. Both types of controllers appear easy to implement in hardware and software. Industrial buffer vessels, natural water reservoirs and physiological systems are stated as potential applications of modelling and control, and extensions to chemical reactors are discussed.

Tracer response functions can be interpreted and used under conditions of variable flow, if they are presented in terms of an integrated flow variable and if the flow patterns are not affected by changes of flow rate. Several flow models are analyzed and they show an agreement with the presented theory. Application of the method to arbitrary non-analytic response and distribution functions is outlined. Use of the distribution functions of presented type for control of variable flow processes is similar to the control of stationary systems. The functions are also applicable to time domain methods for control of variable flow chemical reactors with linear kinetics.

Dynamic model of the rotary grinding mill is described as a superposition of the kinetic grinding process and the flow pattern expressed by the residence time distribution. The model of a system with a closed grinding loop is obtained as a combination of models of its components. Dynamics of the system have been simulated, after the model has been reduced to correspond to a binary grain size distribution. The numerically obtained impulse responses of this multivariable model are now converted to frequency responses, and appropriate controllers are designed by means of the Nyquist arrays. By means of the known, mostly nonlinear dependences of the model on the various inputs, the controllers can be retuned without a physical testing, e.g. after a permanent change of the average value of an input variable has been observed.

Character of work and its change with advance of automation are described for the main professional groups of workers in Finnish concentrators for metal bearing minerals. The workers views and opinions related to work, instrumentation and automation, working environment, health etc. were collected using a questionnaire and tabulated as functions of professional groups ana of levels of automation. It is described to which degree the workers in different professions feel affected by-load of work, by requirements to mental efforts and by motivational content of work, and by their change, in terms of level of automation. Discriminant analysis is applied to the answers which are classified according to the professional groups but regardless of levels of automation. The resulting discriminant functions are characterised by feelings about work and effects of working conditions, and to some degree by mental capacity of worker, by variation of work and by attitudes to automation. When the answers are classified according to the levels of automation, regardless of professions, the obtained discriminant functions are characterized by familiarity with automation, by mental and physical load and by variation and dependence of work.

Steady state models of open and closed grinding loop are derived, Starting from the dynamic model of the rotary mill and the models of the other components. The parameters have been evaluated by sampling and analyses, and by computation based on the models. Use of the models for control and optimization of the steady state is explained. The dynamic models are also studied in order to develop recursive on-line methods for parameter and state estimation. Ooth a general, infinite dimensional state space model and a slightly simpler finite dimensional model are presented and their use for estimation and control is discussed.

An integrated flow variable is introduced. It is shown that the residence distributions and weighting functions of many continuous flow processes which are time-variable under unsteady flow conditions, are invariant with regard to this new variable. Constant parameter controllers may be designed for time-variable processes on the basis of models which are written in terms of this variable, and then methods can be applied which are analogous to the conventional design methods. Assuming the volumetric process flow is measured, the operation of such controller is synchronized to the integrated flow variable instead of the time-base of the conventional controller. The control coefficients of both types of controllers have a simple mutual relationship.

The paper suggests an algorithm for the multi variable control of an lndustrial sulphide ore grinding plant. The algorithm is based on the linear-quadratic gaussian control theory with a moving time horizon The dynamic process model with two inputs and two outputs is obtained by fitting simple low order linear time-invariant models to measured inputoutput pairs. Characteristic to the model are the long time delays The model is brought to the state space form, in which the time delays are described by additional state components. The inputs are replaced by input increments, for the purpose of avoiding imbalances of stationary values and of assigning quadratic costs to input changes Because of disturbances both in the process and in the measuring equipment Kalman filter is used for state estimation. In order to carry out the setpoint changes faster, the estimated state is corrected by tne corresponding change of coordinates The control algorithm has been tested both by simulation runs and by implementation in the control of the plant. The weighting and covariance matrices were used as tuning parameters. The test runs showed that the algorithm can be tuned rather easily and satisfactory results were obtained The steady state interactions were almost completely compensated and the transient interactions were within acceptable limits