As a rapidly growing field, the flowsheeting industry’s fundamental importance to process design is illustrated by its lucrative nature. Flowsheeting software, as with any assumption-based engineering modelling, faces limitations. Digital twins offer potential advancements that could address the limitations of flowsheeting, such as poor modelling accuracy, limited customisation, accumulation of errors, and poor cost estimation. Whilst research has explored unit operation digital twins, there has not been an endeavour to apply them specifically to the limitations of flowsheeting. Therefore, this project aimed to explore the use of digital twins of unit operations to specifically address flowsheeting limitations. In line with achieving this aim, a pump, heat exchanger, and reactor were selected, coded in Python, and subsequently embedded in the open-source flowsheeting software, DWSIM. Data for the digital twins were either sourced from manufacturers or generated in ASPEN, before processing through methods such as neural networks or polynomial regression. The key findings included: the pump library demonstrating a more accurate cost estimation compared to traditional models; the grey box reactor digital twin addressing assumptions of idealised models, improving accuracy; and the heat exchanger’s preliminary success in its application to multiple fluid cases, showing potential to reduce the data required by digital twins. It was concluded that with consideration of the limitations around data availability, paired with further engineering theory implementation, unit operation digital twins have the potential to offer improvements to flowsheeting. Looking at the applications of this potential, from a manufacturer's perspective, digital twins of their equipment could offer compatibility validation and real system performance predictions which would improve customer confidence and, in turn, equipment sales.