Hybrid data-driven and mechanistic modeling approaches for multiscale material and process design

The world’s increasing population requires the process industry to produce food, fuels, chemicals, and consumer products in a more efficient and sustainable way. Functional process materials lie at the heart of this challenge. Traditionally, new advanced materials are found empirically or through trial-and-error approaches. As theoretical methods and associated tools are being continuously improved and computer power has reached a high level, it is now efficient and popular to use computational methods to guide material selection and design. Due to the strong interaction between material selection and the operation of the process in which the material is used, it is essential to perform material and process design simultaneously. Despite this significant connection, the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required. Hybrid modeling provides a promising option to tackle such complex design problems. In hybrid modeling, the material properties, which are computationally expensive to obtain, are described by data-driven models, while the well-known process-related principles are represented by mechanistic models. This article highlights the significance of hybrid modeling in multiscale material and process design. The generic design methodology is first introduced. Six important application areas are then selected: four from the chemical engineering field and two from the energy systems engineering domain. For each selected area, state-of-the-art work using hybrid modeling for multiscale material and process design is discussed. Concluding remarks are provided at the end, and current limitations and future opportunities are pointed out

Gluconic Acid Synthesis in an Electroenzymatic Reactor

AbstractGlucose was selectively oxidized to gluconic acid in a membraneless, flow-through electroenzymatic reactor operated in the mode of co-generating chemicals and electrical energy. At the anode the enzyme glucose oxidase (GOx) in combination with the redox mediator tetrathiafulvalene (TTF) was used as catalyst, while the cathode was equipped with an enzyme cascade consisting of GOx and horseradish peroxidase (HRP). The influence of the electrode preparation procedure, the structural and the operating parameters on the reactor performance was investigated in detail. Under optimized conditions, an open circuit potential of 0.75V, a current density of 0.6mAcm−2 and a power density of 100μAcm−2 were measured. The space time yield of gluconic acid achieved at a glucose conversion of 47% was 18.2gh−1cm−2