This research presents a mathematical formulation for optimising integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The framework is constructed using mixed-integer linear programming (MILP) which exhibits rapid conversion and a global optimum with well-defined solution methods. The framework builds upon previous efforts in process integration and considers materials and energy with thermodynamic constraints imposed by formulating the heat cascade within the MILP. The model and method which form the fundamentals of process integration problems are presented, considering exchange restrictions and problem formulation across multiple time-scales to provide flexibility in solving complex design, planning and operational problems. The work provides the fundamental problem formulation, which has not been previously presented in a comprehensive way, to provide the basis for future work, where many process integration elements can be appended to the formulation. A case study is included to demonstrate the capabilities and results for a simple, fictional, example though the framework and method are broadly applicable across scale, time and plant complexity.

Electricity markets are currently experiencing a period of rapid change. The intermittent nature of renewable energy is disrupting the conventional methods used in operational planning of the electrical grid, causing a shift from a day-ahead forecast policy to a real-time pricing of delivered electric power. A path towards a more renewable, robust and intelligent energy system is inevitable but poses many challenges to researchers and industry. In the field of process industry, strategies based on demand side response are receiving attention and could represent a partial solution for this challenge. Coordination between production scheduling and procurement of electric power is of high importance and can contribute to reducing cost and emissions associated with production. A methodology to quantify such benefits is presented here with a case study, which reveals the potential benefits of flexible operation. In this case, the minimum compensation for flexibility services ranges between 5 and 20 € per unit (MWh) of restricted power. However, such a compensation depends on geographic location (electricity prices) and the frequency of restrictions. The method follows a rolling scheduling approach that provides optimization of the short-term schedule. This work introduces the concept of representing flexible processes as ‘equivalent batteries’ which store electricity from low-cost periods as intermediate products and consume the embedded energy during high-cost periods. Cost related to providing flexibility combined with the profits from optimized process scheduling contribute toward monetization of flexibility as an ancillary service for the grid. Balancing this service with the cost of implementing DSR solutions provides a means for calculating a pricing strategy for grid flexibility.