What is Integrated Process Simulation?

To remain competitive and profitable, it is essential that operations have their process engineering, logistics and integrated supply chains operating at maximum efficiency. This where the use of virtual integrated simulation really adds value.

  • Integrated processes are those that combine more than one specific unit process into a single piece of equipment or into a group of workstations that are operated under unified control.
  • Process simulation is a model-based representation of chemical, physical, biological, and other technical processes and unit operations in software.

Integrated process simulation

The efficient movement and processing of resources, products and materials is central to the success of all mining industry, industrial processes and manufacturing projects today.

The goal of a process simulation is to find optimal conditions for a process. This is essentially an optimisation problem which has to be solved in an iterative process.

Producing invaluable integrated process information that provides clients with informed decision support, gives them the ability to design resilient systems that make sure everything gets where it needs to be, when it needs to be there.

 

The important of understanding your data.

Organisation management is increasingly based on integrated analysis, using data to make informed management decisions. The challenge is making sense of the data and being able to apply it in a logical, visual manner.

While traditional deterministic methods are generally adequate for the design of straightforward operations, applying these methods to more complex operations does not address the levels of dependency within components, nor the inherent variabilities that often characterise modern day supply chains or operations.

When applying deterministic methods, modellers attempt to address uncertainty by representing it with the use of averages or so called “expected” values. The common practice of reducing an uncertainty to a single best guess estimate, unfortunately leads to what is known as “the flaw of averages”, a set of systematic errors that occur when a single number is substituted for a distribution. The resulting miscalculations can have severe consequences in operations and management of projects, which could explain why many projects are behind schedule and over budget.

deterministic vs stochastic

Developing integrated simulations around the use of quantitative models, applying the principles of System Dynamics and probability management (which has its origins in “Monte Carlo” or stochastic simulation), provides organisations with enhanced decision support and a better understanding of the complex characteristics and uncertainty of their operations setting.

By providing a logical, fully integrated virtual representation of a system or process, dynamic simulations enable detailed understanding and assessment of the dynamic variable nature of an environment and the inputs and responses of a system or process.

Siecap’s simulation tools have been successfully applied to:

Siecap's simulation  offering primarily makes use of SIPMath, @Risk and ExtendSim, together forming a multi-domain/platform integrated simulation toolbox , that enables us to model complex continuous, discrete event and discrete rate systems.
  • Supply chain design and capacity assessment
  • Mining pit to port distribution systems
  • Transhipping Analysis
  • Mineral processing
  • Industrial process plants
  • Complex Port Operations (import, export container terminals)
  • System capacity/constraint assessments

CASE STUDY EXAMPLES

mineral processing

Mineral processing

Siecap developed a process model using lab test data and first principle methodologies i.e., mass and energy conservation and the population balance methodology.

simulation mining2

The model allowed the client to:

  • Size equipment (tanks, pumps, pipes & valves)
  • Design process control strategies
  • Hazard and Operability (HAZOP) analyses
  • designing and testing start-up and shut-down procedures
  • de-bottlenecking of operations after the start-up
  • energy use optimisation

simulation risk

@Risk Schedule and Cost Analysis

simulation risk2

Siecap used @RISK in conjunction with MS Project and Excel to model the inherent variabilities in

 the estimated task times and costs as well as the risk probability and resulting impacts on both the schedule and budget in a complex project.

simulation logistics

Simulation Modelling and Analysis of Complex Port Operations

simulation logistics2Siecap developed a discrete event simulation model using Extendsim to model a proposed precinct’s operations. The simulation produced a set of results for different land use scenarios detailing resource utilisation, the total number of trucks passing through, length of queues, average waiting time, etc. all of which served to illustrate each scenario’s ability to address the issue of congestion within the new proposed port precinct.

simulation port

Mining Pit to Port

simulation port2Siecap developed a complete pit-to-port model inclusive of mine processing operations, land operations (hauling, stockpiling, etc), and river/marine operations (ship loading/unloading, inbound/outbound movement to transhipping anchorage, etc).

  • Process Engineering and operations i.e. mass flows, water balance, separation efficiencies, maintenance, breakdowns, etc.
  • Met-Ocean Environment i.e. tides, weather (wind & wave), bathymetry, etc.
  • Logistics i.e. fleet sizes, availabilities, utilisation, maintenance, breakdowns etc.

 


References:


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