Industrial engineering systems view

A checklist of deviations has been developed to support accident investigations. It is based on an industrial engineering systems view and underlines the relationship between accident control and production control, Table 5.1. 

On the basis of an industrial-engineering system s view, taxonomies have been developed that focus on the control of the production system from a management point of view (Johnson 1975, Adams 1976). These models are especially relevant to studies of the control of initial-phase deviation that falls under the purview of management responsibility. The Management Oversight and Risk Tree (MORT) was developed in logical tree format and provides a systematic approach for use in detailed investigations conducted by safety experts (Johnson 1975). 

I.I. The Traditional Safety Engineering (TSE) View The traditional safety engineering view is the most commonly held of these models in the CPI (and most other industries). As discussed in Chapter 1, this view assumes that human error is primarily controllable by the individual, in that people can choose to behave safely or otherwise. Unsafe behavior is assumed to be due to carelessness, negligence, and to the deliberate breaking of operating rules and procedures designed to protect the individual and the system from known risks. 

Production-inventory systems are one of the most established subjects in industrial engineering. The focus is on studying inventory dynamics, with inventory viewed as a buffer between supply (production/replenishment) and customer demand. Hence the emphasis is really more on inventory than on production, the latter being the primary focus of other IE subjects, such as scheduling and production planning. 

Klatt K-U, Marquardt W. Perspectives for process systems engineering - Personal views from academia and industry. Comput Chem Eng 2009 33(3) 536—50. 

Abstract This chapter introduces the context and aims of this book. In addition, it provides a detailed description of industrial production systems including their life cycle, stakeholders, and data integration challenges. It also includes an analysis of the types of intelligent engineering applications that are needed to support flexible production in line with the views of current smart manufacturing initiatives, in particular Industrie 4.0. 

Production resource capabUities can be provided by humans or machines. Figure 2.9 in Chap. 2 shows the example of a lab-size production system. Chapter 2 provides a more detailed view on industrial production systems and the engineering process of these production systems. 

In contrast to automation systems in industrial environments, the simulation framework has a much more complex view of data. A simple occurrence of each variable is expected in industrial automation systems, whereas multiple occurrences are required in the simulation framework. Typical industrial automation systems provide an actual value as well as historical records for each variable. Other occurrences of same variables may be present in the simulation framework. Another occurrence may originate in a simulation as a result or can be prepared by a control engineer manually to describe and represent expected behavior or status. 

This chapter shows how a biphasic medium can help in reducing loss of volatile compounds in a gaseous phase exiting from a bioreactor, in comparison with pure aqueous systems. It also emphasises the usefulness of solvents having low vapour pressure (heavy organic solvents or ionic liquids) in the reduction of the release of compounds into the environment. There are, from this point of view, common interests between engineering needs and environmental concerns in the flavouring industry. 

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