Major process industries have installed onsite the utility systems that can
generate several types of utilities for meeting the utility requirements of the main
production systems. A traditional sequential approach is typically used for the
planning of production and utility systems. However, this approach provides
suboptimal solutions because the interconnected production and utility systems
are not optimised simultaneously. In this research, a general optimisation
framework for the simultaneous operational and maintenance planning of utility
and production systems is presented with the main purpose of reducing the
energy needs and resources utilisation of the overall system. A number of
industrial-inspired case studies solved show that the solutions of the proposed
integrated approach provides better solutions than the solutions obtained by the
sequential approach. The results reported a reduction in total costs from 5% to
32%. The reduction in total costs demonstrate that the proposed integrated
approach can result in efficient operation of utility systems by avoiding
unnecessary purchases of utility resources and improved utilisation of energy and
material resources. In addition, the proposed integrated optimisation-based
model was further improved with the presence of process uncertainty in order to
address dynamic production environment in process industries. However,
integrated planning problems of production and utility systems results to large
mixed integer programming (MIP) model that is difficult to solve to optimality and
computationally expensive. With this regards, three-stage MIP-based
decomposition strategy is proposed. The computational experiments showed that
the solutions of the proposed MIP-based decomposition strategy can achieve
optimal or near-optimal solutions at further reduced computational time by an
average magnitude of 4. Overall, the proposed optimisation framework could be
used to integrate production and utility systems for effective planning
management in the realistic industrial scenarios.PhD in Energy and Powe
