Solution: Minealytics Neural Reagents Optimal Control

Minelatyics' predictive control, powered by our neural model, can forecast overflow pH, clarity and underflow density from the available historical process data. The model constantly adapts to any relevant process changes by continuously learning from new data. 

The reagents additions is optimised via a genetic algorithm that uses the neural model for finding the optimal addition of flocculants or coagulants. 

A genetic algorithm (GA) can be an effective method to optimize the addition of reagents in a thickening process by utilizing a neural model that predicts key parameters such as clarity, overflow pH, and underflow density. This approach combines the adaptive capabilities of genetic algorithms with the predictive power of neural networks to achieve optimal process control in mineral processing.

The Minealytics Thickener neural network model is trained on historical and real-time process data to predict the outcomes (clarity, overflow pH, and underflow density) based on various input variables, including the type and amount of reagents added, feed rate, particle size distribution, and slurry pH. The model learns complex relationships between inputs and outputs, enabling accurate predictions of the process outcomes under different conditions.

The Minealtyics Reagents Optimiser is inspired by the process of natural selection, which mimics the biological mechanisms of evolution such as selection, crossover, and mutation. In the context of reagent addition, the GA iteratively searches for the optimal set of input variables (types and dosages of reagents) that lead to the desired outcomes (optimal clarity, pH, and density) by the following process:

1. Initialization:

   - The GA starts with a population of potential solutions, each representing a different combination of reagent types and dosages.

2. Evaluation:

   - Each solution in the population is evaluated based on a fitness function. The neural model predicts the outcomes (clarity, pH, and underflow density) for each solution, and the fitness function assesses how close these predictions are to the desired operational targets.

3. Selection:

   - Solutions with higher fitness scores (those that result in outcomes closer to the desired targets) are more likely to be selected for the next generation.

4. Crossover and Mutation:

   - Selected solutions undergo crossover and mutation operations to create a new generation of solutions. Crossover combines features from two or more "parent" solutions, while mutation introduces random changes. These operations introduce variability and help explore a broader solution space.

5. Iteration:

   - This process repeats over several generations, with the GA evolving towards solutions that increasingly meet the desired process outcomes.

6. Convergence:

   - The algorithm continues until it converges on a solution (or set of solutions) that optimises the addition of reagents to achieve the desired clarity, pH, and underflow density, or until a predefined number of generations is reached.

The GA can adapt to changes in the process or feed characteristics, optimizing reagent addition in real-time. By exploring a wide range of possible solutions, the GA can find optimal or near-optimal reagent dosages that might not be intuitive or apparent through traditional methods. The Minealytics Thickener Neural model can capture complex, non-linear relationships in the process, which the GA can leverage to optimize reagent addition even in complex scenarios.

Technology

Genetic algorithms (GAs) are a class of optimization algorithms inspired by the principles of natural selection and genetics. They simulate the process of natural evolution, employing mechanisms such as selection, crossover (recombination of genetic material), and mutation to evolve solutions to problems over successive generations. A GA begins with a population of randomly generated individuals, each representing a potential solution encoded as a string or sequence (analogous to chromosomes). The fitness of each individual is evaluated based on how well it solves the problem at hand. The fittest individuals are more likely to be selected to pass their genes to the next generation. Over time, the population evolves towards an optimal or near-optimal solution. GAs are particularly useful for solving complex optimization problems where traditional analytical approaches are inefficient or infeasible.