Process Engineering Optimization
Optimization is the art of finding the best possible result under the given circumstances
The search for the best solution
At the heart of process engineering is the search for the best solution, the maximum , the minimum or, more generally, the optimal solution.
For example: To produce a determined cheese from a determined amount of milk within a determined time with a determined effort within a determined infrastructure.
Our natural inclination to tackle and solve such optimization problems is expressed in terms such as "cost-efficient", "most for least" and "more return for the money".
Often people talk about optimized solutions without specifying what exactly is being optimized. The task of optimization is to maximize or minimize something. Optimization always takes place in relation to a specific size/goal.
The term "optimal" always refers to the best solution with regard to an optimization criterion, taking side conditions into account. The optimization criterion is formulated as a so-called objective function , which depends on variables of the process. The "best solution" corresponds to a extreme value of the target function. In process engineering, non-linear models are often used to describe basic operations or entire plants, which can lead to multiple extreme values.
Optimizing a process is generally sensible if the changes made can save costs, improve quality, safety and efficiency, and if these changes can be carried out safely. To assess whether optimizing a process/system is worthwhile, you need to understand how planned changes will affect other parts of the system.
In the case of cheese, there is a feedback loop from the solution to the problem. The cheese produced must be good and meet the requirements of food legislation.Optimizing system parts
A system is optimized by changing individual components. For example, the reduction in cooling water consumption could be improved. This requires the installation of a new, larger heat exchanger and changes to the control system. The new heat exchanger requires more space, and the change to the control system could have an impact on other parts of the process control system.
Many optimization tasks already arise during the production of the system and therefore determine the process. For example, the distribution of membrane modules to individual stages is an optimization task. Complicated optimization tasks arise in particular during ongoing processes, such as improvements in cleaning (CIP).Functional vs. optimized
In the development / production of technical systems, a distinction must be made between designs for a functional system and individual, optimized functions of a system.
When designing a functional system, engineering focuses on fulfilling the basic requirements and functions in order to create a product that meets the requirements. The manufacturer receives the requirements from a specification sheet (and confirms them with the functional specification).
In contrast, a system with optimized functions refers to the optimization of already functioning properties, for example by — maximizing yield or minimizing water consumption.
A functional system fulfills the defined requirements of the system purpose and adheres to the defined specifications (functional specification).
In the long term, however, it is rarely enough to guarantee functionality alone. Operating costs are always at the expense of insufficiently optimized systems. Every system has weaknesses and every process can be optimized. Aspects that do not appear clear at the start of a project often only become clear after commissioning and in the course of several productions. This is when engineering services are required that go beyond mere functionality, as optimizable goals must be achieved.
The transition from a functional system (or parts thereof) to an optimized system is an iterative process. While the design of a functional system provides the basis, optimization enables fine-tuning to achieve a maximum or minimum in terms of specific functions. This two-stage approach meets the basic requirements and maximizes operational benefits.