We propose a mixed-integer nonlinear programming (MINLP) model for simultaneous utility and heat exchanger area targeting with variable stream conditions. The model represents the composite-curve-based area targeting method by constructing the hot and cold composite curves mathematically. We introduce a "dynamic" enthalpy grid onto which the stream inlet/outlet temperatures and enthalpies are mapped. By calculating the temperatures at each grid point and the stream heat duties at each interval, the utility consumption and heat exchanger areas are simultaneously optimized using an economic criterion. We discuss preprocessing methods tailored to aid the solution of the proposed MINLP model. The model is applied to two illustrative examples as well as an example where it is integrated with a process synthesis model.

We present a framework for the formulation and solution of continuous process scheduling problems. We focus on modeling transient operations such as startups, shutdowns, and transitions between steady states. First, we show how the concept of processing tasks can be generalized to represent continuous processes, including their transient operations. Second, we discuss how to systematically calculate the parameters describing material consumption/production and utility consumption during transient operations. Finally, we present new mixed‐integer linear programming formulations for the scheduling of continuous chemical production.

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We present a general mixed-integer programming model for periodic production scheduling. The formulation, which is based on the state-task network (STN) representation, allows us to model (1) accurate inventory levels, (2) various inventory policies, (3) different demand patterns and profiles, and (4) flexible assignment of tasks to units. We first develop a model for batch processes and extend it to handle continuous processes. The model addresses all the limitations of previous approaches to periodic scheduling and allows us to address various types of periodic scheduling problems. Finally, we demonstrate the performance of the model when applied to different types of processes through several case studies.