Retrofit optimization

Note that for circumstances where operational conditions are changing, for instance in a combined oil and gas field where relative volumes of gas, oil, and seawater, as well as molecular wei ts are changing, the two audit types may occur simultaneously if retrofit, optimization, or redesign become an issue. 

Various criteria were proposed for the optimal selection of the equipment configuration and the number and sizes of units. In grass-root design, the capital cost of equipment is mostly used as the optimization criterion. In retrofit design a more appropriate objective function seems to be the net profit, which has to be maximized. Papageorgaki and Reklaitis (1993) formulated the criterion as follows ... 

If the capital cost of new heat transfer area is expressed in the form of Equation 18.6, then this will lead to poor retrofit projects. The problem with Equation 18.6 is that the optimization is likely to spread the new heat transfer area in the network in many locations, without incurring a cost penalty associated with the many modifications that would result. To ensure that new heat transfer area is not spread around throughout the existing heat exchanger network, a capital cost correlation should be used that is of the form ... 

The approach leads to simple and practical retrofit designs and has the major advantage that it allows the designer to assess modifications one at a time and to keep control over the complexity of the retrofit. Its disadvantage is that different combinations of modifications can be taken and there is no guarantee that this will lead to an optimum network retrofit. However, it is almost impossible to say that any retrofit is optimal or nonoptimal. The features of each retrofit are unique and it is difficult to formulate all the constraints for a retrofit in order to guarantee the very best retrofit. 

Zhu XX and Asante NDK (1999) Diagnosis and Optimization Approach for Heat Exchanger Network Retrofit, AIChE J, 45 1488. 

As with the case for water minimization, the graphical methods used for effluent treatment and regeneration have some severe limitations. As before, multiple contaminants are difficult to handle, constraints, piping and sewer costs, multiple treatment processes and retrofit are all difficult to handle. To include all of these complications requires an approach based on the optimization of the superstructure. 

Complex steam systems usually feature many important degrees of freedom to be optimized. To establish the steam costs for retrofit of site processes requires an optimization model to be developed. This allows the steam loads for process heating to be gradually decreased and the steam system reoptimized at each setting. The result in cost... 

Some recent applications have benefited from advances in computing and computational techniques. Steady-state simulation is being used off-line for process analysis, design, and retrofit process simulators can model flow sheets with up to about a million equations by employing nested procedures. Other applications have resulted in great economic benefits these include on-line real-time optimization models for data reconciliation and parameter estimation followed by optimal adjustment of operating conditions. Models of up to 500,000 variables have been used on a refinery-wide basis. 

Briones, V. and A. Kokossis. A New Approach for the Optimal Retrofit of Heat Exchanger Networks. Comput Chem Eng 20 (Suppl) S43-S48 (1996). 

Pistikopoulos, E. N. and I. E. Grossmann. Optimal Retrofit Design for Improving Process Flexibility in Nonlinear Systems—I. Fixed Degree of Flexibility. Comput Chem Eng 13 1003-1016 (1989). 

Madron, E (1992). Process Plant Performance. Measurement and Data Processing for Optimization and Retrofits. Ellis Horwood, Chichester, England. 

In a retrofit batch design, we optimize the batch plant profitability defined as the total production value minus the cost of any new equipment. The objective is to obtain a modified batch plant structure, an operating strategy, the equipment sizes, and the batch processing parameters. Discrete decisions correspond to the selection of new units to add to each stage of the plant and their type of operation. Continuous decisions are represented by the volume of each new unit and the batch processing variables which are allowed to vary within certain bounds. 

The nonlinear nature of these mixed-integer optimization problems may arise from (i) nonlinear relations in the integer domain exclusively (e.g., products of binary variables in the quadratic assignment model), (ii) nonlinear relations in the continuous domain only (e.g., complex nonlinear input-output model in a distillation column or reactor unit), (iii) nonlinear relations in the joint integer-continuous domain (e.g., products of continuous and binary variables in the schedul-ing/planning of batch processes, and retrofit of heat recovery systems). In this chapter, we will focus on nonlinearities due to relations (ii) and (iii). An excellent book that studies mixed-integer linear optimization, and nonlinear integer relationships in combinatorial optimization is the one by Nemhauser and Wolsey (1988). 

Proper machine cycle settings are also important for energy savings. The injection speed control system (available on new machines as an option or as a retrofit on old machines) optimizes the speeds available for specific time intervals. The injection high volume pump is available for a fixed time period and is then dropped out of the system. This time period should coincide with... 

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