The chemical industry has some of the most important enabling technologies for modern society. Their products cover all sectors ranging from plastics, pharmaceuticals, energy and fuels, to food, agriculture and the environment. Practically all aspects of modern life are driven by products generated from the chemical industry. At the heart of the chemical industry are chemical reactors (which are vessels wherein chemical reactions take place). Chemical reactors are the key pieces in converting (less useful) raw materials into end user products.
How well these chemical reactors work is vital in determining how efficient the processes are that use them. In most cases, lower efficiency results in larger carbon dioxide emissions, which is both harmful and wasteful to society*. Given that the chemical industry (directly) consumes roughly 5% of the world's energy, the efficient design and opertation of chemical reactors is an important goal for modern industrial processes.
Chemical reactions rarely produce pure products. This inefficiecy results in separation and purification steps having to be included into a chemical process. But separation and purification are often use the most energy demanding steps in a process. Energy efficiency and process simplifications can result if reactors are designed to produce products that minimize or even avoid separation altogether.
Choosing the best set of reactor(s) for a given system of reactions and kinetics is often a nontrivial task. Let us consider some of the questions and challenges that might be encountered.
The problem of designing and optimizing a collection of reactors in an optimal manner—for a desired duty—is called the reactor network synthesis problem.
Consider a reactor configuration that has already been designed and optimised, such as the configuration given below:
How do we know that it is the best? If there are no other designs to compare to the current design, then there is little certainty that the current design is the best. Other reactor configurations (that we have not yet thought of) might exist that perform better.
The problem is that it is always possible to devise new designs that might do better. And we will not know if these new designs are better until each design is compared to every other design. This problem can be solved if an independent and common measure is available through which all current designs can be assessed against. In essence, we need to establish appropriate performance targets.
The AR is the collection of all possible outputs for all possible reactor configurations—even the ones we cannot imagine.
Every point in or on the AR is an output of a reactor combination. Different parts of the AR boundary correspond to distinct reactor combinations. Using the AR, we can compare the performance of different reactors in relation to each other as well as in relation to the boundary of the AR, which represents the absolute best performance of the reactor configuration.
AR theory helps designers understand how to interpret, select and optimise a network of chemical reactors, so that the flowsheet is appropriate for a desired duty, because different duties may favor different reactor structures, and hence it makes sense to choose the most appropriate reactor combination for the required duty. When the objective changes, so should the structure.
Hence, AR theory is a method for helping to address the reactor network synthesis problem, although it also assists in understanding and setting appropriate performance targets.
Using AR theory, we can begin to understand two important problems:
The absolute limits of the reactor system, which is useful for performance targeting of new and existing designs of chemical reactors.
The physical equipment required to achieve these limits, so that we know how to approach and optimize our designs. This problem is also known as the Reactor Network Synthesis (RNS) problem.
Understanding these two aspects helps us to design efficient, robust and simpler reactor networks
Designing a network of reactors is challenging because often a large number of variables and design choices must be considered before a suitable design can be created. Furthermore, even if an existing design is available, there always exists an element of uncertainty, for there may be alternate designs (which we have not thought of) that perform better. AR theory is a method that helps to address this problem. Using AR theory, one can systematically determine all potentially feasible products for a given system, and in the process learn to distinguish between superior and inferior designs. This approach also ensures that some (potentially superior) designs are not left unconsidered.
The next section describes the basics of AR theory.