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R. Schiemann et al.
Digital Process Know-How and Its Usage in Advanced Plant
Operation
With process know-how being the identified key success factor in digital solutions,
the remainder of this paper will focus on describing a digital system that integrates
extensive process know-how, design experience, and advanced operational control
into one holistic solution. The pure advanced automation aspect of such a solution alone can quickly achieve process stabilization, but true long-term optimization
of plant production, availability, and profitability is only achievable through the
embedded know-how of process thermodynamics and equipment design details.
As the technology provider for a certain process must have a comprehensive
know-how on all aspects of this technology regarding the design, construction, commissioning, and operation of the plant, it is a logical step to integrate an advanced control philosophy into this scope and responsibility. This serves as a basis for potential
operational improvements and is hence integrated into digital optimization solutions.
The process know-how can be integrated into different model-based digital systems, such as heat and mass balance calculation models [2], dynamic process models
[3], or equipment and process level continuous monitoring systems [4]. Making these
models available to the plant site enables short- and long-term improvement of the
operating conditions. For sulfide ore roasting applications, automatic recognition of
sulphur input to the furnace and accordingly prevention of bed overfeeding is a very
good example of how a digital system can prevent highly undesired, potentially hazardous operating conditions. A further beneficial integration of process know-how is
in automated operational sequences, which are especially common during plant load
changes or start-ups and shutdowns. This reduces the risk of human errors potentially leading to an abortion of the load change, reducing overall plant availability
and lifetime of the plant equipment.
Roaster Optimizer as Part of a Digital Solution Concept
As discussed in this paper, the integration of process and equipment know-how is
a key success factor in complementing advanced automation, leading to optimizing
solutions at a plant level. Figure 1 shows the general scheme of process knowhow integration. As depicted, the Pretium suite includes different product classes
(Advisor, Optimizer, Training simulator, Plant performance monitor), each of which
has its own benefit realization approach and set of features. All of them have in
common that the process knowledge, operating data, laboratory results, design data,
and process models are combined and transferred to a virtual plant, which forms the
backbone for all mentioned products. Such a system can also be used for a real-time
comparison between measured data and the data expected by the know-how enabled
software system. By performing such a so-called gap analysis, untapped potential in
the plant can be revealed and equipment or process issues can be identified.
R. Schiemann et al.
Digital Process Know-How and Its Usage in Advanced Plant
Operation
With process know-how being the identified key success factor in digital solutions,
the remainder of this paper will focus on describing a digital system that integrates
extensive process know-how, design experience, and advanced operational control
into one holistic solution. The pure advanced automation aspect of such a solution alone can quickly achieve process stabilization, but true long-term optimization
of plant production, availability, and profitability is only achievable through the
embedded know-how of process thermodynamics and equipment design details.
As the technology provider for a certain process must have a comprehensive
know-how on all aspects of this technology regarding the design, construction, commissioning, and operation of the plant, it is a logical step to integrate an advanced control philosophy into this scope and responsibility. This serves as a basis for potential
operational improvements and is hence integrated into digital optimization solutions.
The process know-how can be integrated into different model-based digital systems, such as heat and mass balance calculation models [2], dynamic process models
[3], or equipment and process level continuous monitoring systems [4]. Making these
models available to the plant site enables short- and long-term improvement of the
operating conditions. For sulfide ore roasting applications, automatic recognition of
sulphur input to the furnace and accordingly prevention of bed overfeeding is a very
good example of how a digital system can prevent highly undesired, potentially hazardous operating conditions. A further beneficial integration of process know-how is
in automated operational sequences, which are especially common during plant load
changes or start-ups and shutdowns. This reduces the risk of human errors potentially leading to an abortion of the load change, reducing overall plant availability
and lifetime of the plant equipment.
Roaster Optimizer as Part of a Digital Solution Concept
As discussed in this paper, the integration of process and equipment know-how is
a key success factor in complementing advanced automation, leading to optimizing
solutions at a plant level. Figure 1 shows the general scheme of process knowhow integration. As depicted, the Pretium suite includes different product classes
(Advisor, Optimizer, Training simulator, Plant performance monitor), each of which
has its own benefit realization approach and set of features. All of them have in
common that the process knowledge, operating data, laboratory results, design data,
and process models are combined and transferred to a virtual plant, which forms the
backbone for all mentioned products. Such a system can also be used for a real-time
comparison between measured data and the data expected by the know-how enabled
software system. By performing such a so-called gap analysis, untapped potential in
the plant can be revealed and equipment or process issues can be identified.