The challenge

In large integrated manufacturing plants, significant quantities of process by-products can accumulate onsite due to the presence of trace elements that limit their reuse, either impacting on process operation, affecting product quality, or reporting to regulated emission streams. Byproduct material that cannot be reintroduced into the process represents not only a lost opportunity for reuse, but also an increasing potential liability. Within an integrated steelworks, byproduct reuse onsite has been confirmed as a way of reducing the financial liability associated with stockpiled residues. It is therefore important that key modelling tools are developed to fully assess the impact of byproduct reuse and how this can be optimised, allowing mitigation or even reversal of waste accumulation.

The BlueScope Port Kembla Steelworks (PKSW) generates numerous byproduct streams. Some of these byproducts are reused within the process, offsetting the use of raw materials, or sold offsite for reuse. Some byproduct streams are restricted in their reuse due to the presence of key trace elements such as lead and zinc. PKSW have operational guidelines that limit reuse of some byproducts to within certain limits. These guidelines are based on operational experience and empirical evidence gathered in plant trials, and the effect on downstream trace element concentrations.

To date, strategies for the reuse of byproduct materials have not been based on a modelling and optimisation approach.

The response

A collaborative project is underway between University of Queensland, the ARC’s Steel Research Hub based at University of Wollongong and BlueScope (BSL) in Port Kembla.

As part of the project, a reuse-focused optimisation model is being developed to maximise byproduct reuse that could:

  • Be fine-tuned based on plant trials at the steelworks,
  • Be flexible enough to be extended to various byproduct reuse scenarios, and
  • Incorporate the key trace elements that limit reuse.

The model includes ten key trace elements critical to assessing byproduct reuse. This model will aim to predict trace element behaviour across key process units, allowing reuse scenarios at BSL’s site to be investigated within the context of an optimisation problem.

A comprehensive model of PKSW’s processes has previously been developed based mainly on mass and energy balances. That model was used as a basis to create the optimisation model, using the same modelling platform (Aspen Custom Modeler).

The sinter plant (SP) was selected as the initial focus of the study. The SP takes fine iron ores, various fluxes, coke and other fine materials to produce an agglomerated burden material for the ironmaking blast furnace (BF). From a byproducts perspective, the SP is a logical unit operation for processing such materials within the steel plant and is often used to do so.

A stand-alone model of the SP was developed to model byproduct introduction into this unit. More recently, BSL undertook a successful trial introducing one particular byproduct material into the SP, allowing for the possibility of validating the optimisation model based on the trial results. The addition of byproducts to the SP was successfully modelled using this stand-alone model.

The impact

The ultimate objective is to deliver a reuse-focused optimisation model that will allow BSL to strategize its use of byproducts, and reduce both the cost of raw materials, and waste accumulation at BSL’s PKSW site.

The standalone sinter plant model developed in the early phase of the project will be extended to include the Blast Furnace (BF) and basic oxygen furnace (BOF).

Together with the SP, the BF and BOF are crucial processes within the integrated steelworks for both formation and reuse of byproducts.

Incorporation of the thermodynamics and trace element partitioning across these key process units will allow better representation of the PKSW processes, allowing byproduct reuse scenarios to be developed for situations where the trace element content is a constraint. This will allow specific investigations within the context of an optimisation problem.