Equipment cost algorithms

Example 21.1 and 21.2 illustrate the use of the algorithms to obtain equipment cost data. 

The same algorithm presented above is used to estimate bare module costs for all equipment. This information is listed in Table E7.14fb along with purchased equipment cost, pressure factors, material factors, and bare module factors. 

A convenient way to display cost-capacity data is by algorithms. They are readily adaptable for computerized cost estimation programs. Algorithm modifiers in equation format may be used to account for temperature, pressure, material of construction, equipment type, etc. Equation (9-2) is an example of obtaining the cost of a shell-and-tube heat exchanger by using such modifiers. 

Step 5. Economic evaluation. Not shown in this example, but included in a process synthesis program such as PIP, are algorithms for costing and economic evaluation of the process. Process equipment is sized and priced and total plant investment is estimated. Requirements and costs for utilities and raw materials, other operating costs, and product values are estimated. These values are used to evaluate the profitability of the proposed process and can provide a sound basis for a more detailed design. 

Cost data were obtained from a number of different sources and are referred to for each algorithm in Table 21.2. All algorithms have been updated to the first quarter of 2003 and the cost data were checked with equipment manufacturers data. Any cost index may be used but the Chemical Engineering Index found in the magazine Chemical Engineering is satisfactory for costing equipment. 

A new methodology designed to optimize both the planning of preventive maintenance and the amount of resources needed to perform maintenance in a process plant is presented. The methodology is based on the use of a Montecarlo simulation to evaluate the expected cost of maintenance as well as the expected economic loss, an economical indicator for maintenance performance. The Montecarlo simulation describes different failure modes of equipment and uses the prioritization of maintenance supplied, the availability of labour and spare parts. A Genetic algorithm is used for optimisation. The well-known Tennessee Eastman Plant problem is used to illustrate the results. 

In Table 23.10, Kemp (1998) gives resnlts that were obtained from the proprietary dryer selection algorithm developed by Separation Processes Service (SPS) of AEA Technology, Harwell, United Kingdom. Althongh only one choice is reported here, it shonld be noted that, in most cases, alternate dryers can also be recommended with nearly equal performance. If local cost of equipment and energy are factored in along with the valne of the dried product itself, the results may be different as well. 

Optimization is performed solving a Multiobjective Optimization Problem (MOP) using as optimization algorithm a customized Sequential Quadratic Programming (SQP) method (Fletcher 1987). The solution of the MOP provides the Pareto front of the problem which, in our application, consists of 155 optimal solutions. However, as inputs of the equipment reliability and cost models fluctuate according to distribution laws reflecting uncertainty on parameters, objective functions will fluctuate also in repeated runs. 

Eq. (5) represents the total cost given by the investment and utility cost. In this way, the objective function in Eq. (5), subject to constraints in Equations 1 to 4, defines a mixed integer linear programming problem. The numerical solution to the MILP problem can be obtained with standard algorithms. In Eq. (5) QJ, PCk and FC,s are the heat loads of crystallization or dissolution, the heat loads of evaporation, and the variable costs and fixed costs for the equipment associated with task t of multiple saturation point s. 

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