Conversion of plastic waste into hydrogen is a potential solution to address the issues of growing demand for hydrogen and the massive accumulation of plastic waste simultaneously. However, most studies on plastic-to-hydrogen technology selection were based on predetermined plastic waste composition, limiting their applicability to dynamic real-life operations. To address this, this work introduces a flexible optimisation model capable of accommodating varying compositions of plastic waste. With the aid of regression models, the optimisation model can optimise the plastic-to-hydrogen production pathways, considering economic and environmental performances, without the constraints of specific plastic waste types or mixture compositions. Regression models are developed based on the ultimate analysis data (carbon, hydrogen, nitrogen, oxygen, and sulphur content) to estimate hydrogen yield and purity across various pathways. Thereafter, fuzzy optimisation is employed to identify the trade-off between cost and environmental impact. In addition to the selection of optimal plastic waste-to-hydrogen pathways, the model also considers different purification technologies that can improve the hydrogen purity to various extents. The model demonstrated that pyrolysis-steam reforming combined with PSA is capable of achieving hydrogen purity of 99.999 % with a highest overall satisfaction of 0.7141 (equivalent to total cost of 3.43 M$ and emissions of 528,647 kg CO2/y) while pyrolysis-catalytic decomposition is more suitable to produce hydrogen with lower purity (55 %).

The rise in biodiesel production results in an excess of crude glycerol, which further leads to environmental concerns. Consequently, transforming crude glycerol into valuable products is deemed an effective way to address this issue. Process Integration techniques are introduced to enhance the overall economic viability by maximizing the energy recovery in the biodiesel plant. However, most of the existing studies merely focused on a single optimal heat exchanger network (HEN) generated. In this study, P-HENS software is utilized to generate viable HENs for a glycerol-derived 1,3-propanediol plant. Subsequently, piping costs of each HEN are estimated to determine the optimal HEN by assuming the respective heat exchanger is placed at the centroid. Finally, the optimal HEN is identified based on the total annualized costs (TAC) (which include the capital cost of the heat exchanger, utility cost, and piping costs) and energy-related carbon emissions of the network. The results show that, among the 4,188 feasible networks generated, network #623 possesses the best overall performance when both cost and environmental aspects are considered. The carbon emissions of network #623 is 16.7% lower than that of the case without heat recovery. This work demonstrates the usefulness of the generated near-best HENs in enabling a more comprehensive HEN optimization. By application of the proposed methodology, the most economical and environmentally friendly HEN can be determined. This contributes to both cost savings and sustainability in HEN design.