In the past few years, rapid strides have been made in the modelling of complex systems due to the advent of machine learning (ML) technologies. In particular, the transformer neural network (TNN) has gained a lot of attention due to its powerful “sequence-to-sequence” modelling for several tasks in science and engineering. Predictive ML models are trained to learn the relationship between a set of input and output data so that new input data can be mapped to their expected output values. This gives us the ability to understand the key factors impacting a system and to make predictions about its future behaviours. This research investigates how to use TNNs for decision making by optimising the output of a TNN. To this end, a non-convex mixed-integer non-liner programming (MINLP) formulation for a trained TNN is proposed. The proposed formulation facilitates solving problems with TNNs embedded
to global optimality. The effectiveness of the formulation is tested on three case studies consisting of an optimal trajectory problem, a verification problem, and a reactor optimization case study. Results show that optimisation over small TNNs can be achieved in under 3 minutes. However the tractability of the formulation quickly vanishes for larger models, highlighting the need for further research to refine the proposed formulation.

The modern chemical industry faces many challenges, such as energy transition. However, energy transition alone will not provide enough improvements to the industry to maintain profitability and increase sustainability. To achieve these goals, chemical processes have to be appropriately optimised, and only the synergy of these two factors can improve existing processes. Epichlorohydrin production is an important industrial process, but it suffers from several drawbacks such as high energy consumption, significant wastewater production, and low atom efficiency. This is caused by chlorohydrin-based technology, which requires operations in very diluted solutions. In this thesis, a novel chlorohydrin-free technology for ECH production was investigated. This approach could allow operation in more concentrated solutions, but this route is in the early development stage. One of the most crucial design parameters for this process is proper solvent selection. On the one hand methanol appears to be the most suitable compound for this purpose. On the other hand, some papers reported the separation system for this case to be infeasible due to several azeotropes present in the post-rection mixture. However, with proper constraints and understanding of components' azeotropic behaviour, a separation system, which enables obtaining high purity ECH, was created and applied to the production process. To perform a comparison between HP route and chlorohydrin process an Aspen Plus simulation of both processes were created. For the hydrogen peroxide route with methanol as solvent a novel separation system which enables high purities of ECH were created. Furthermore, possibilities to optimise distillation in the given process were investigated because this unit operation requires significant expenses in terms of CAPEX and OPEX. A review of advanced distillation techniques concludes that Dividing Wall Column distillation is the most suitable technique for this purpose. This technology was then applied to replace two columns, which purifies the intermediate Allyl Chloride (ACH) from the process. Aspen Plus simulations of both processes with and without applied DWC distillation were created to evaluate the influence of these improvements. Moreover, to establish the impact of DWC distillation, an Aspen Plus model of this apparatus was created. Simulation results indicate that this novel epoxidation reaction produces 98% less wastewater than the traditional process. Additionally, the novel approach offers a 10% higher yield and a smaller amount of by-products than the chlorohydrin process. Energy consumption per unit of ECH is also lower for the novel route. Application of DWC distillation led to 3.5% decrease in OPEX, while the CAPEX was smaller by almost 5%. These results indicate that applying a novel epoxidation route and DWC may benefit a given plant. However, more research needs to be performed to implement a novel process in the industry.