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The main benefits expected from module-based plant design are shortened planning periods and increased planning as well as cost estimation accuracy during early engineering phases. Most of the existing approaches require equipment module databases. However, overall equipment module databases do still not yet exist. Even though these databases will be available, they will most likely not contain operable equipment modules for any process task. Thus, the objective of this work is to increase the accuracy of investment cost estimation without the need of equipment module databases, which is exemplified for plate as well as shell and tube heat exchangers. Instead of selecting one equipment module from an existing module database for a certain process task, all operable equipment modules are generated by determining design parameter combinations, which are located inside the design space and thus fulfill all operating constraints. Tailor-made equipment design is not needed in any step. The detailed investment cost estimates of all operable equipment modules are used to determine the range of possible investment cost. The variance of the investment cost estimates determined is equal or less than half of the error of conventional preliminary investment cost estimation in case of 54% of the industrial process tasks.

Current challenges for (bio-)chemical industry require shorter project lead times. To reduce the lead time, the concept of module-based plant design was developed. Suitable equipment modules are selected from an equipment module database instead of time-consuming tailor-made equipment design. The quality of module-based plant design strongly depends on the quality of the equipment module database. An equipment module database should contain the least number of equipment modules covering as many industrial applications as possible. Hence, the generation of an equipment module database is a maximum coverage problem known from other disciplines, such as facility location. Within this work, the corresponding maximum coverage problem is formulated and solved by a randomized greedy algorithm. The approach for the generation of an equipment module database presented is exemplarily applied for liquid/liquid heat transfer applications provided by Evonik . It is shown, that less equipment modules are required for the same coverage of applications if the equipment module database is generated based on the maximum coverage problem than based on the existing methodology presented by Eilermann et al. (2017). Additionally, less computational effort is required.

Increasing economic challenges lead to the need for faster plant design in process industry. In this context, a promising approach is module-based plant design. Thereby, for the accomplishment of the required design tasks modules are selected from databases and configured instead of time-consuming and tailor-made plant design. Within this work, a general approach to module-based plant design is introduced and illustrated based on an example. To structure the design procedure different types of modules are defined for different design tasks: PFD, P&ID, Equipment and 3D Layout. Additionally, module selection can be performed at different Levels of Aggregation to cope with the high variance of applications. This enables module selection and configuration avoiding time-consuming, tailored modifications. Since modules are unmodifiable and project-independent, the module-based plant design approach presented is based on a consistent module definition. This work provides a framework to integrate some of the existing modularization approaches into a general module-based plant design approach. However, most often new approaches are necessary to accomplish the design tasks within the presented module-based plant design approach. Thus, this work also identifies open research gaps that need to be filled by future research.

Modular plant design is an approach for making chemical production more flexible and more efficient. Different approaches for modular plant design have been developed, for example in the CoPIRIDE or F3 factory project. They have in common, that lead time reductions for modular equipment are expected e.g. by utilizing design repetition or parallelization of preassembly of modules. To support the decision for or against a modular concept, besides cost effects possible lead time changes compared to conventional concepts should be anticipated in early economic evaluations already. In this article, a lead time estimation method will be presented that correlates project costs and project durations and can be applied to modular and non-modular plants enabling comparative studies. An example from a previous paper was used to investigate the impact of modularization on lead time. It includes modular production lines and a non-modular backbone facility that provides energy and utility supply. A range of investment sizes (FCI of 3–95 mio. €) was investigated and compared with a conventional reference plant. Total lead time reduction was in the range from 2.6 to 5.5 month depending on investment size. For a more significant impact on the lead time the modularization approach needs to be modified by also applying modular design to the backbone facility. In this case depending on investment size total lead time reduction would be between 3.9 and 18.7 months representing a very significant reduction of 23%–60% compared to the lead time of the conventionally designed reference plant. This is considered as the maximum expectable lead time reduction that can be achieved through modular plant design. This reduction would represent a major potential for speeding up construction of chemical plants.

As market demand forecasts will become more uncertain in the future it is necessary to develop new methods for plant design. New technologies allow combining the flexibility of a batch plant with the efficiency of a continuous production. To gain even more economic benefit intensified equipment is often designed as a module. The usage of equipment modules allows an easily and efficient increase of capacity. In this way a plant expansion close to the market is possible. Consideration of stepwise plant expansion is often limited to the copying of complete production lines. This approach results in high additional investment costs due to the loss of economy of scale. To overcome the limitations set by economy of scale, an equipment wise expansion strategy should be applied. By debottlenecking the capacity limits of the plant it is possible to adapt plant capacity very close to the changing environment and reduce the additional costs. Therefore, design of a modular plant must be combined with suitable economic evaluation methods for uncertain demand forecasts. In this work a framework for such a design based on predefined and standardized modules will be presented. The framework consists of two stages. The first stage is the selection of possible modular setups and suitable expansion strategies. Next these setups are evaluated in a real option analysis to investigate the economic performance in an uncertain market. The approach will be used to evaluate a multiproduct continuous plant. The main finding is that the economy of the modular plant depends under the given boundary conditions on the equipment item with the highest proportion on total investment costs and a high cost degression exponent.