ENGLISH VERSION: CONTINUOUS PROCESSES MODELING THROUGH MES ANSI/ISA-95 ORIENTED SOLUTIONS – PART 2
Continuing this post series regarding an ANSI/ISA-95 oriented MES modeling, specific for inventory management (stockpiling), this part emphasizes how production is measured and then transported to product stock yards.
It continues using iron ore production as reference.
How production is defined in a continuous process?
In continuous processes, production is defined in such a different way if compared to batch and discrete processes, where main product characteristics are easier to determine.
To do a parallel between an iron ore “lot” and lot of parts (discrete process) we can make some key questions:
- Where is the lot? Considering a part, it is pretty easy to define
- What are lot characteristics? Considering a part, it is easy to define as well (color and dimensions for instance)
- What is lot amount value? For a part lot, just count it
For an iron ore lot, these questions are very much more complex, but not impossible to answer. Conjunction of several information sources and a close to real process modeling can yield to a very useful representation for inventory and storage control and simulation of a final product dispatch planning and scheduling for customers.
Unlike batch processes, where product is easily determined for production plant setup and bill of consumption materials, in some continuous processes with several production output products, this final product definition is more complex.
Obliviously, for iron ore production, product definition is also given by production plant setup and bill of raw materials. It happens that making these factors stable and as planned is a very difficult task.
Process inherent characteristics with high stock in progress brings a lag into setup changes propagation along the whole process. Another matter is that actual chemical composition of ore in extraction fronts is frequently different from planning besides other unpredicted variations, such as moisture (raining) and intermediate stockpile composition, as an example.
Production responses and result lots dimensions
Product material is pre-determined for a specific control point (production measurement spot – Stacker A, for example) as an expectation. Nevertheless, “quality” (iron ore characteristics) is only given after lab results have been acquired and then, real product definition is stated.
Amount value is regularly given through mass integration scales installed on conveyor belts at stackers, for example. Along well known and defined time spans, these amount values are then summed, generally using PIMS tools and the accounted into MES solutions.
Quality definition is attainted through lab results. Lab tests are made using samples got and linked to same production time spans and measurement spots used to calculate production amount values.
Position is determined by inherent physical building constraints of production control points, such as stackers, which are designed for small product production sets and in some circumstances, only one. These stackers generally have a restrained and pre-determined reach for storing production. For a specific position, it is necessary to define an exact stacker position via high precision GPS or manual input, given by operator using MES solutions.
After defining those three dimensions (position, quality / characteristics and mass), prodution result is determined and production lot defined.
ANSI/ISA-95 oriented modeling
In a summary representation, it is possible to model those discussed process operations and production / inventory phases with “Production Information” ISA-95 concepts and definitions.
Product mass accounting and summing interval result in production registration itself, “Production Response“ and ”Resource Actual”.
Product (“Material Definition”) is defined as the one pre-determined (expected) for that stacker which is further reclassified if necessary.
After getting quality and position information, lot context and production result (“Segment Response”) are completely defined.
Lot quality characteristics are represented as a set of “Material Lot Property”, such as %Fe and %SiO2.
Do you want to know more about it?
Next part of this series will address product yard internal operations for handling stockpile lots.
Watch for the next post.
Produtos Digitais
Confira outros artigos
Produtos digitais: entregas contínuas com IA
Nas últimas semanas, lançamos uma série de artigos sobre o uso de inteligência artificial no processo de construção de produtos digitais. Neles, apresentamos alguns aceleradores que a dti tem utilizado para potencializar a eficiência dos times. Abordamos a fase de concepção do produto, as atividades de gestão e design, o desenvolvimento do software e a […]
Produtos Digitais
Inteligência Artificial: acelerando o design e gestão de produtos digitais
Como aproveitar o melhor da Inteligência Artificial Generativa para gerar mais valor? Essa tem sido uma pergunta recorrente no mercado conforme as empresas buscam entender e adotar a tecnologia. Embora existam muitas dúvidas e hipóteses não comprovadas, parece ser consenso que os avanços na Inteligência Artificial impactarão significativamente muitas profissões. No relatório The economic potencial […]
Produtos Digitais
IA Generativa: acelerando a concepção de produtos digitais
Como as empresas podem responder às crescentes demandas por produtividade e eficiência em um mundo digital em constante mudança? O período pandêmico acelerou a digitalização de várias empresas e agora o desafio é outro. Entramos em uma era de incertezas, na qual não há mais espaço para desperdícios ou prolongados ciclos de entrega de software. […]
Produtos Digitais