Process engineers operating chemical and metallurgical processes face various challenges to analyse, manage, and optimise complex processes in highly competitive markets. Insights into various aspects of the processes are required to support informed decision making to improve the operation of existing processes, and for the design of new processes or modifications thereof.

Engineers are challenged with some of the following questions and decisions needed to be made as informed and efficiently as possible:

  • What is/was/will be the process performance?
  • What would be the best option of process/equipment modification?
  • How could the process performance be optimised?
  • What was the root cause of the failure of a process aspect, and how could it be corrected?
  • What would the influence of changing a process variable be on other variables and overall process performance?
  • What changes in manipulated variables are necessary to obtain control objectives?
  • What are the relationships between variables on the equipment/process level and the business/organisational level?

To answer these questions requires insight into physical phenomena of the process, gained through the application of fundamental principles and methodologies in analytical and modelling studies. Sustainable benefit is obtained from the knowledge generated when it can be deployed into organisational systems either as tools that could be repeatedly used, or as advanced control systems.

Algoness provide services and solutions in which the required knowledge, expertise, and fundamental principles and methodologies are combined. Algoness also has the ability to utilise different software technologies creating efficient platforms for the development and deployment of process intelligence. In all cases a well thought through and methodological approach is followed to define, develop, implement, and control all project outcomes.

Some examples of the projects that we would be involved in include:

  • Statistical data analyses for data mining and predictive modelling purposes.
  • Reconciling mass and energy balances.
  • Continuous/discrete mass and energy balance modelling.
  • Process modelling of thermochemical phenomena.
  • Finite element heat and mass transfer modelling.
  • Inferential process variable modelling of physio-chemical properties.
  • Development of advanced modelling and control techniques (e.g. artificial intelligence).
  • Knowledge transfer (training) of engineers providing practical engineering application skills.

To find out more on whether we are able to make a contribution to your process, please contact us.