Production processes outputs

Operating parameters of this German plant, on the basis of one cubic meter of raw gas, iaclude 0.139 m O2, 0.9 kg briquettes, 1.15 kg steam, 1.10 kg feed water, 0.016 kWh, and 1.30 kg gas Hquor produced. Gasifier output is 1850 m /h and gas yield is 1465 m /t dry, ash-free coal. The coal briquettes have a 19% moisture content, 7.8% ash content (dry basis), and ash melting poiat of 1270°C. Thermal efficiency of the gas production process is about 60%, limited by the quaHty and ash melting characteristics of the coal. Overall efficiency from raw coal to finished products is less than 50%. 

The purpose of the control plan is to ensure that all process outputs will be in a state of control by providing process monitoring and control methods to control product and process characteristics. The control plan is covered in section 6 of the APQP manual. It consists of forms containing data for identifying process characteristics and helps to identify sources of variation in the inputs that cause product characteristics to vary. The APQP manual provides excellent guidance on the compilation and use of the control plan so no further guidance is given here. 

Following production launch, process capability and performance should be measured continually in order to demonstrate that your processes remain capable and the capability index continues to rise. Appropriate action should be taken on characteristics that are either unstable or non-capable. Action plans should be implemented to contain process output and continually improve performance. 

A failure modes and effects analysis is a systematic analytical technique for identifying potential failures in a design or a process, assessing the probability of occurrence and likely effect, and determining the measures needed to eliminate, contain, or control the effects. Action taken on the basis of an FMEA will improve safety, performance, reliability, maintainability and reduce costs. The outputs are essential to balanced and effective quality plans for both development and production as it will help focus the controls upon those products, processes, and characteristics that are at risk. It is not the intention here to give a full appreciation of the FMEA technique and readers are advised to consult other texts. 

Process Characterization— The determining of relationships between process parameters and process outputs or product characteristics. 

In practice, production processes are usually rather more complex. Raw materials are usually impure and thus some pre-purification steps may be required. Obviously impurities in the raw materials will incresae the probability of impurities and byproducts occuring in the output stream from the chemical conversion step. Even using pure raw materials, most chemical conversion are incomplete and often lead to the formation of undesirable byproducts. Furthermore often additional (auxiliary) materials are used (for example catalysts, specific solvents), which have to be separated from the desired product. Thus, in typical production processes a large number of separation steps are required. 

The costs of an intervention have to be compared with the results of this intervention (Drummond et al. 2004). These results can be outputs, outcomes, and impacts (Fig. 2). An output is the direct result of a production process. Agents of production (resources) are transformed to generate a certain commodity or service (output). For instance, equipment, reagents, and the knowledge of a laboratory technician are used to perform a certain resistance test. Other examples of outputs are contacts, admissions, or prescriptions. 

A health economic evaluation calculates the efficiency of the transformation processes. For instance, we can compare the consumption of agents of production with the output, the outcome, or the impact of this production process. Table 1 demonstrates some possible comparisons and indicators. 

Inputs and outputs assessed in mass balancing are shown in Figure 5.3. The software EATOS was used to calculate all mass balances of processes. Outputs of EATOS are the mass index (equation (5.1), mass of raw material per mass of product output), and the environmental factor (equation (5.2), mass of waste output per mass of product output). EATOS also allows the calculation of cost indices (e.g., reference [15]) (equation (5.3), cost of raw material per mass of product output). 

Mathematical models are relationships in the form of mathematical expressions which describe the dependence of a process output (yield, product properties etc.) upon process variables, which include ... 

Another LCI-related issue is allocation. This is especially relevant to waste treatment options when waste is recycled or used to generate energy, there is a multi-output process which needs to be dealt with. The waste then is no longer considered a waste, but a resource, and waste treatment is becoming a production process as well. What part of the emissions to allocate to the waste treatment service and what part to the secondary material or other co-product is then open to debate. ISO allows for various options. Whether additives are even visible in such processes is, again, doubtful. 

The stoichiometric data is included in order to perform material balances in each unit operation. The second column of the stoichiometric data shows the amount of raw material required (tons) per unit mass (tons) of the overall output, i.e. s6 + si + s8. The third column shows the ratio of each byproduct (si and. S 8 ) to product (s6) in ton/ton product. The objective function is the maximisation of product (56) output. A 20% variation in processing times was assumed. 

In particular results on tank capacity are a typical output of simulations on the plant engineering level. It could be argued that these results may be obtained without simulation as well and this is true as long as the stochastic impact on supply and demand is within certain boundaries. As soon as the facility needs to be able to handle stochastic supply and demand with significant variations static calculations reach their limits. These limitations become even more critical if a multi-product process is analyzed as quite often is the case. 

Figure 10.7 shows the extended RTN formulated for the benchmark problem. The production process includes diverging and converging material flows, flexible proportions of output goods (task Tj), cyclic material flows (recycling of output from task T3 into state Si), intermediate products which cannot be stored (state nodes S5, S9, S10, S12), and blending of products in task Ti 5. All processing tasks are operated batch-wise with lower and upper bounds on batch sizes. Batch sizes are... 

Input products are single to few and factors are mainly stable. Input factors are reflected in the recipe. Recipe in the chemical industry is a synonym for the bill-of-material in discrete parts manufacturing and includes all input products with their respective input fraction required to produce one unit of one or several output products in a production process. In chemical production, the degree of raw material consumption rates and hence the recipe factors can depend on the processing mode of the equipment, which can be employed at different utilization or throughput levels. In this case, the recipe is not composed of static input factors but of recipe functions, which express the relationship between the input consumption and the process quantity produced. 

RIO - Variable production processes, input and output planning Production processes have variable run times and throughputs as well as multiple input and output products as illustrated in fig. 49. 

Production planning has to determine run time and throughputs of production processes in order to derive input and output quantities for related input and output products (see also Franke 2005). Production processes can run between a minimum and maximum utilization (Siirie 2005b, p. 213). Production processes apply for continuous single purpose assets as well as... 

RIO Variable production processes, input and output planning ... 

Production planning of quantities and campaigns described in subchapter 5.6 has to consider the variable production processes with multiple input and output and throughput smoothing as the planning of change-overs between campaigns. 

A product hierarchy comprises product line, product group, product and article. Product line and product group combine products by common characteristics on two aggregation levels. A product is the direct input or output of a production process articles include also packaging information. Therefore, one product can be filled into multiple articles. 

Input products required in a production process have the same index set structure like output products r,s,p e IPU groujps all processes and input products according to the resource V r,s e y, pe I. Combinations of processes, input products, resources and locations are defined by r,l,s,p G Ipu, V r,/ e/P4, r,s,p e Ipu. Secondary demand for input products is aggregated on a location level based on the index set... 

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