Process Knowledge

In manufacture the common denominator is task and process is expressed through task. The tasks are the external expressions of process and they reflect the process knowledge involved. At the highest level process knowledge is expressed as words and we use words to define actions that are invariably performed on some objects. Actions and objects come together to form the process narrative, which is at the core of process design. Let us look at an example  

We are familiar with these expressions as being related to the making of tea or coffee, we need to, however, look at them from a different perspective. In the instructions we find that the actions are Add and Pour, and the objects are Sugar or Milk the former are verbs the latter are nouns. This grammatical parse of the narrative to isolate the actions and the objects involved helps us to bring to the surface the structural essence of process description [1]. We can rearrange the narrative to see more clearly the associations amongst the actions and the objects involved and the sequence of events, as shown below  

There are several things that can be noticed in this arrangement. Firstly, the attributes of the objects influence the attributes of actions secondly, the Add and the Pour actions have motion and equipment requirements. The profile of motion is dictated by the attributes of sugar and within this profile is inherent the quality of the task and also the time involved to perform it. The description Add Sugar is a macro task, whilst the action profile is a micro task and the sequence of events ensure the precedence. This provides the mechanism to structure and visualise process information as shown below  

Process information is the first level of visualisation of macro parameters it lies midway between process knowledge and process data. The translation of process knowledge into process information is the main activity of process design it translates the word picmre into detailed action format. The issues after that centre more on data development and management. It is at the interface of process knowledge and process information that the heuristics offer the greatest potential, and at this interface we translate abstract knowledge into concrete information. 

Starting at the top, the assembly data object has one-to-many type of relationship with the part data object, whilst part object has one-to-one type of relationship with the assembly object. This determines the schema for the assembly objeet and bill of materials (BOM) is an example of it. The part object has one-to-one type of relationship with the task sequence object and it is the same viee versa. In practical terms this means that there is always a unique sequenee of tasks to manufacture the part dictated by the material and volume involved. The task sequence object has one-to-many type of relationship with the task object and the task object has a one-to-one type of relationship with the task sequence. This means that a task sequence often contains many different types of tasks. Finally, the task object has one-to-many type of relationship with the material object and equipment object, whilst the material and the equipment objects have a one-to-one type of relationship with the task object In practical terms this means that a task often utihses many different types of materials and equipment to make the parts. The determination of these relationships together with the object attributes lead us to the schema design that can hold all the information required for the manufacturing process design. The data relationships model of Fig. 3.2 integrates the key variables involved and their interactions 

---

Away from the pinch there is usually more freedom in the choice of matches. In this case, the designer can discriminate on the basis of judgment and process knowledge. 

The theoretical energy requirement for the burning of Portiand cement clinker can be calculated from the heat requirements and energy recovery from the various stages of the process. Knowledge of the specific heats of the various phases, and the heats of decomposition, transformation, and reaction then permits calculation of the net theoretical energy requirement of 1760 kj (420 kcal) for 1 kg of clinker from 1.55 kg of dry CaCO and kaolin (see Clays) (8). 

Short development time Allocate enough time for development may result in a less than, more time-efficient PEIA techniques complete knowledge of the hazards administrative controls to decide when to go to full scale production Establish minimum requirements transfer package for process knowledge Require development chemist to be present during initial product runs API RP 750 CCPS G-1 CCPS G-10 CCPS G-25... 

The basis for your program plan should be your team s assessment of the current PSM status (Figure 4-11), which will have yielded a list of deficiencies compared with the required PSM elements. These gaps must now be translated into statements of required tasks, which in turn suggest work products for inclusion in your implementation plan. For example, your assessment shows that the process knowledge and documentation management system needs improvement. To address this gap you need to plan a series of tasks. 

Process knowledge and documentation Define P IDs needed Establish management system for future updates... 

Before starting the search for solutions, it is necessary to select among the M decision variables a subset of H variables, Xf, h- 1,..., W, which influence significantly the system performance, and thus will be used by S and included in the definition of the final set of hyperrectangles, X. For this preliminary choice of critical decision variables, other than his or her own specific process knowledge, the decisionmaker can count on a number of auxiliary techniques enumerated in Saraiva and Stephanopoulos (1992c). 

Labels are distinguished based on whether they are context dependent or context-free. Context-dependent labels require simultaneous consideration of time records from more than one process variable context-free labels do not. Thus, generating context-free trend, landmark, and fault descriptions is considerably more simple than generating context-dependent descriptions. Context-free situations can take advantage of numerous methods for common, yet useful, interpretations. Context-dependent situations, however, require the application of considerable process knowledge to get a useful interpretation. In these situations, performance is dependent on the availability, coverage, and distribution of labeled process data from... 

Principal component regression (PCR) is an extension of PCA with the purpose of creating a predictive model of the Y-data using the X or measurement data. For example, if X is composed of temperatures and pressures, Y may be the set of compositions that results from thermodynamic considerations. Piovoso and Kosanovich (1994) used PCR and a priori process knowledge to correlate routine pressure and temperature measurements with laboratory composition measurements to develop a predictive model of the volatile bottoms composition on a vacuum tower. 

Additional insight is possible from Fig. 38b. Here we see that the magnitude of the explained variance accounted for by the second PC has noticeably increased after minute 70. This is consistent because, from process knowledge, it is known that removal of water is the primary event in the first part of the batch cycle, while polymerization dominates in the later part, explaining why the variance profile changes around the 70-minute point. 

To get a deeper insight into the metabolic and energetic processes, knowledge of general principles of chemical energetics appears to be of help. 

Thus, for hazard identification, only the measurement of one or two temperatures is necessary. Actually, for equipment without a heating or cooling system, evaluation of the term (d2T/dt2) greater than zero is sufficient. The method is independent of detailed process knowledge and, generally, of human judgment. 

Process Knowledge and Documentation—The main features here are process definition and design criteria, process and equipment design, company memory (management information), documentation of risk management decisions, protective systems, normal and upset con-dtions, and chemical and occupational health hazards. 

The adjustment of measurements to compensate for random errors involves the resolution of a constrained minimization problem, usually one of constrained least squares. Balance equations are included in the constraints these may be linear but are generally nonlinear. The objective function is usually quadratic with respect to the adjustment of measurements, and it has the covariance matrix of measurements errors as weights. Thus, this matrix is essential in the obtaining of reliable process knowledge. Some efforts have been made to estimate it from measurements (Almasy and Mah, 1984 Darouach et al., 1989 Keller et al., 1992 Chen et al., 1997). The difficulty in the estimation of this matrix is associated with the analysis of the serial and cross correlation of the data. 

SEGE is unable to give a perfect identification with the current process knowledge in the following situations ... 

Collect Chemical Reactivity Data Process Knowledge and Documentation Process Safety Information Identification of Major Hazards... 

Document Chemical Reactivity Risks and Management Decisions Process Knowledge and Documentation Process Risk Management Process Safety Information Process Hazard Analysis Operating Procedures Operational Control... 

Both preserving and making available this process knowledge within a company are important (CCPS 1989), in order to ... 

This section provides guidance for creating the element of a chemical reactivity hazard management system that will ensure the capture and retention of safety-related process knowledge and documentation. The components of this element are ... 

---