State-based Material Waste Quantity Simulation

Heilala et al. (Heilala et al. 2008, pp. 1928) presents the SIMTIR framework, which uses an operating state based discrete simulation to predict energy and resource consumption. Multiple material waste flows are calculated, yet it is unclear if the material waste is allocated to the energy-relevant operating states: idle, down, busy, repair (Heilala et al. 2008, pp. 1928).

Duflou et al. (Duflou et al. 2012, pp. 590) presents an inventorization method to allocate material waste, operating materials consumption, and power consumption to process specific “production modes”. Each production mode represents a single activity or operating state observed on a machine of specific technology, e.g. Sintering production modes: process exposure,

preheating, cooling, recoating, cooling down, and cleaning. Based on the production mode measurements a DES Simulation can be performed.

While this method has its merits in increasing the degree of detail of material waste flows to that of power consumption, it fails to address the differences between material waste and energy consumption by adopting production modes based solely on the power consumption behavior. Secondly, the relationship between the production modes is unclear, e.g. which conditions trigger each production mode? A technology-specific approach can be cumbersome for manufacturers, as it requires a long analysis of machine data, video material, or employee protocols to identify distinct production modes and to allot the material consumption to each one.

Alvandi et al. (Alvandi et al. 2015) simulates material and energy flows at the multi-machine level of manufacturing systems, although the focus is on operating materials. The work builds on the premise that there is a characteristic material consumption or a material consumption profile for a manufacturing process in a specific machine operating mode. The approach borrows the operating mode definitions from the energy consumption modelling approaches of Haag, Dietmaier et al., Verl et al. (Dietmair et al.

2008; Dietmair et al. 2008; Verl et al. 2011; Haag 2013). The method assumes the mode-specific material waste profiles are static, ignoring the effect of time-dependent factors (e.g. exhaustion) or external disturbance factors (e.g.

temperatures). However, the significance of mode transitions is acknowledged.

Sheehan et al. (Sheehan et al. 2016) builds on the premise of operating-mode based modelling, extending the definition of machine operating modes to describe inventory stockpiles. Analogous to a characteristic energy profile, a characteristic material waste profile is allocated to each operating mode.

However, since operating mode transitions are particularly turbulent for machine stability, a lump material loss is assumed for each operating state transition, independent from the material waste profiles. This effect is ignored in the analogous energy efficiency work, because the increased energy consumption is negligible (Haag 2013). Individual material waste profiles are determined for each waste form and each mode or intermodal transition via measurement. Sheehan et al. mentions that Haag’s energy-relevant operating states: work, warmup, wait, block, error, setup, off/standby, and save, are not relevant to material efficiency in their entirety, though certain modes, like setup, have a different type and quantity of material consumption. A set operating mode chart determines which transitions are possible under which conditions.

Hopf (Hopf 2016, pp. 81) presents an operating state oriented modelling method for both energy and material resources in production systems. The machine finds itself in one of three main operating states at all times:

operation, no operation (off), and standby. While transitions between main operating states are infrequent, perhaps only once a day, the machine frequently switches between sub-states in the main operation state. These consist of work-ready (waiting), operation-ready (idle), work (and process specific work variations), preparation, error, startup and shutdown.

Hopf’s approach distinguishes between the short-interval operating states and the long interval states, an obvious but important observation in the understanding of machine dynamics, which has been neglected in other works.

However the reasoning for the differentiation, e.g. to the required planning for an early a machine-shut-off vs. machine idling is not specifically presented.

Like the other works, a generic structure for operating state logic, or a set of conditions, under which an operating state is maintained or changed, is

is solely justified through load profiles, not a distinct material consumption or waste occurrence patterns.

Table 6: Operating state structure and origin in material efficiency simulations

Body of

work Operating states Transitions Origin of

states State logic Heilala et al.

2008 idle, down, busy, repair Not stated Not stated Not stated Duflou et al.

2012

Process-specific, e.g.

exposure, preheating, cooling, recoating, cooling down, and cleaning

Included as state

Process

observation Not stated

Alvandi et al. 2015

Pre-production, production, post-production, ramp-up, failure, off, change-over, ramp-up, pre-production,

Modelled / Included as State

Observation metalworking process

Petri-net based, conditions not states

Sheehan et al. 2016

Off, work, error, idle, setup

Modelled separately with lump material quantity (e.g.

startup losses)

Eliminating non-material relevant operating states from Haag (machine control)

State-diagram with conditions

Hopf 2016

No operation: standby;

operation: work-ready (waiting), operational (idle), work (+ process specific work variations), preparation, error, startup and shutdown

Included as state

Energy consumption profiles

Possible transitions highlighted, no conditions

In Table 6, the differences between the machine operating state-oriented simulation approaches are summarized. Comparing the selected operating states and their justifications, four commonalities between the methods arise which are described in detail below.

1.Considerable number of operating states: significant variation in material waste compositions or quantities under different conditions warrants the definition of a separate operating state. If the measured waste quantities are nearly identical, the additional measurement effort must be weighed against the benefit of multiple operating states. Practical application of the methods requires measurement of each material waste- machine-operating

state-product variant combination. If additional lump material waste sums occur when transitioning between operating states (e.g. starting up, or suddenly idling), the number of measurements quickly escalates.

2.Tailor-fit to energy modelling: due to the current trend to model material consumption as a small aspect of resource consumption, approaches tailor-fit to energy consumption are transferred to material consumption with minimal adaptation. The selection of operating states has been chosen based on their characteristic energy loads, not material consumption. Multiple operating states may be identical with respect to material efficiency, while characteristically different operating states for material may have been ignored or lumped with others.

One of the most common and expensive material waste forms, startup losses after setups, cannot be allocated to any of the described operating states and requires the modelling of transitions between operating states, which is often neglected in energy efficiency modelling (Haag 2013, pp. 74).

3. Operating states vary, are technology specific, or experience-based:

both different terminology as well as varying definitions are used to describe operating states, making it unclear which set is the most accurate and concise.

Some sets are clearly only accurate for a single technology, such as those presented in Duflou et al., while other are seemingly generic but based on experiences with machine controls on certain types of technologies.

4. Unclear operating-logic: while some author’s provide a diagram of the possible transitions and the conditions to make a state-transition, there is no consensus on a generic operating state logic. A generic state logic is necessary for practitioners to have a starting point for modelling their production system, if the real conditions for state- transitions are unknown or too complex for the simulation.