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.

Recently, ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP) enabling surrogate-based optimisation in various domains as well as efficient solution of machine learning verification problems. However, previous works have been limited to multilayer perceptrons (MLPs). The Graph Convolutional Neural Network (GCN) model and the GraphSAGE model can learn from non-euclidean data structures efficiently. We propose a bilinear formulation for ReLU GCNs and a MILP formulation for ReLU GraphSAGE models. We compare our formulations to a Genetic Algorithm (GA) by comparing solution times and optimality gaps while modelling a dataset of boiling points of different molecules. Our method guarantees to solve optimisation problems with trained GNNs embedded to global optimality. Between our two formulations the GraphSAGE neural network achieves similar model accuracy, and achieves faster solving times when embedded as a surrogate model in an MILP problem. Finally, we present a computer aided molecular design (CAMD) case study where the formulations of the trained GNNs are used to find molecules with optimal boiling points.