Intelligent process control using predictive and pattern recognition techniques

A device for controlling an electrochemical cell having an electrolytic bath and a cell condition comprising:

• an algorithm for predictive control of the cell;
• and
• an algorithm for pattern-recognition control of the cell;
• and
• a neural network which utilizes the pattern-recognition algorithm to recognize and identify the cell condition;
• a neural network which utilizes the predictive algorithm to predict the cell resistance and the rate of change of the resistance over time;
• and
• a controller for controlling the rate at which reactant is fed into the cell, wherein the controller comprises a feeding control logic, and further wherein the feeding control logic utilizes at least pattern-recognition and predictive control methods.

A device for closed-loop control of the production of aluminum in electrochemical cells having a resistance and a cell condition comprising:

• a neural network for predictive control of the resistance of the cell;
• a neural network for pattern-recognition control of the condition of the cell;
• a feeding control logic controlled by the neural networks;
• and
• a controller for controlling the rate of addition of a reactant to the cell according to the feeding control logic;
• wherein the resistance and the cell condition change over time and wherein the controller controls the cell to operate efficiently independent of the condition of the cell by using non-linear regression functions to deduce the concentration of reactant in the cell and using the predicted resistance and the condition of the cell to control the feeding of the cell in sufficient time to optimize the feeding by optimizing the reactant concentration in the cell.

A device for closed-loop control of an electrochemical cell having an electrolytic bath containing a reactant, and a cell condition, the device comprising:

• a neural network for predictive control of the cell which predicts future values of resistance in the cell and the rate of change of the resistance over time;
• a neural network for pattern-recognition control of the cell which recognizes the present condition of the cell;
• output from both neural networks;
• a codebook of triggers, cell conditions and reactant concentrations;
• and
• a controller for controlling the rate at which reactant is fed into the cell, wherein the controller utilizes a feeding control logic, the codebook and the output.

A controller for controlling the rate at which a reactant is fed into an electrochemical cell comprising the steps of:

• identifying a set of typical conditions in the electrochemical cell;
• measuring the resistance and alumina concentration for each of the conditions;
• plotting the curves characteristic of each condition;
• associating a nonlinear regression to each curve;
• training a first neural network to predict future values of the resistance in the cell and the rate of change over time of the resistance;
• training a second neural network to recognize the present condition of the cell;
• establishing a codebook of triggers, cell concentrations and alumina concentrations to be used by a control logic;
• and
• utilizing the first and second neural networks, the codebook and the control logic to control the electrochemical cell.

A method for determining the optimal alumina concentration in an electrochemical cell having a cell condition during the electrochemical production of aluminum comprising the steps of:

• training a first neural network to predict values of the cell resistance and a second neural network to recognize the present condition of the cell;
• identifying typical conditions of an aluminum electrochemical cell and associating a characteristic curve to each cell condition;
• associating a nonlinear regression function to each curve from which the resistance versus alumina concentration may be determined;
• establishing a codebook of triggers, cell conditions and alumina concentration;
• using the neural networks, the codebook and a control logic to perform closed loop control of the cell under low, medium and high alumina concentrations;
• comparing the performance of the cell under the low, medium and high alumina concentrations;
• and
• determining the optimal operational value of the alumina concentration in the cell.

A method for closed loop control of an electrochemical cell for producing aluminum, comprising the steps of:

• using two levels of control, a first control level comprising predicting a cell resistance and its rate of change over time, a second control level comprising recognizing at least one cell condition, each condition having a characteristic curve described by a non-linear regression function;
• estimating a real-time alumina concentration from the, non-linear relationship of resistance versus alumina concentration;
• establishing a set of decision criteria based on the cell condition, the estimated alumina concentration and the predicted values of the cell resistance and its rate of change over time;
• and
• feeding alumina into the electrolytic bath based on the set of decision criteria.

A device for controlling an electrochemical cell having an electrolytic bath resistance and a cell condition comprising:

• means for predicting the cell resistance and its rate of change over time;
• means for recognizing the cell condition;
• and
• means for feeding a reactant into the electrolytic bath based on a set of decision criteria, wherein the set of decision criteria are based on the cell condition, an estimated real-time reactant concentration, and the predicted values of the cell resistance and its rate of change over time, and wherein the real-time reactant concentration is estimated from a non-linear relationship of the cell resistance versus reactant concentration.

A device for controlling an electrochemical cell having an electrolytic bath resistance and a cell condition comprising:

• a first neural network that predicts the cell resistance and its rate of change over time;
• a second neural network that recognizes and identifies the cell condition;
• and
• a feed controller that sets the rate at which a reactant is fed into the cell, wherein the feed controller comprises a feed control logic, and wherein the feed control logic utilizes at least the first and second neural networks.