- PAPUA NEW GUINEA (PORT MORESBY): 7-11/7/25
Course Brief
The aim of this course is to introduce participants to a strategy to optimise the performance of a mineral processing plant. This strategy is based on a “Nine Step” approach for delegates to implement when looking at their operations.
The Nine Steps to Optimisation are:
- Describe the flowsheet
- Collect data
- Collate data (into a database)
- Mass balance
- Infer unmeasured data
- Define operation parameters
- Model fitting
- Simulate
- Optimise
The course will include numerous examples, principally using Microsoft Excel and Access. For this course, the instructor will provide software. In addition, delegates will be able to make us of ExcelFlow, a Mass Balance and Simulation software, which is provide on a 12-month trial basis to delegates.
Attending the Fundamental to Advanced Microsoft Excel course, offered by DEKA Dynamics prior to attending this course is strongly recommended.
The course will start with a series of preliminary concepts, before continuing to investigate the Nine Steps.
The four preliminary concepts are:
- Overview of the course – includes using Excel as a flowchart simulator. The difference between a design simulator and an operation simulator.
- Mathematical foundations, with focus on:
- Constrained optimisation.
- Least squares optimisation.
- Interpolation as a basis for machine learning.
- Review of statistical concepts: mean, variance, covariance, principal component analysis.
- Using Excel Solver.
- Overview of VBA – Visual basic for Application – a software language available via Excel.
The course will then focus on the Nine Steps, namely:
- Describing the flowsheet
- Collecting data
- Collating data – Often useful data is stored in Excel workbooks and can be difficult to extract, particularly if considering change over time. In order to store the data in a more structured manner, there are key advantages in using relational databases. The database used in this course is Microsoft Access, which is generally easily available to most mineral processors. Hence this module includes:
- Complete Mass Balance – Mass Balancing here means reconciliation the data so it is consistent. The module content includes:
- Infer unmeasured data – Information theory is a subject that is particularly suited to mineral processing to solve numerous problems, particularly inferring unmeasured data, to construct a set of data suitable for simulation. This module is therefore a unique distinction of this course.
- Defining operational parameters
- Model fitting – Although the course focuses primarily on machine learning methods, it is not a diverse machine learning course. Instead, the course focuses primarily as information theory, as a basis for machine learning, using reinforcement learning.
- Simulation – With guidance from the instructor, delegates will create their own simulator. Delegates may wish to make use of their own flowcharts when completing the simulation.
- Optimisation
What will delegates achieve with this seminar?
The purpose of this course is to provide sufficient knowledge that delegates may advocate a different approach to management, with the view that a structured sampling and analysis campaign could lead to optimisation in the process operation.
What will the delegates employer achieve with this seminar?
Over a period of many years, it has been observed by the instructor that plant surveys tend to be a rare event, with inefficiencies often missed. By applying what has been learned on the course, companies can gain insight into how to improve plant operation via plant surveys.