## Energy Efficiency in Process Plants with emphasis on Heat Exchanger Networks: Optimization, Thermodynamics and Insight

##### Doctoral thesis

##### Permanent lenke

http://hdl.handle.net/11250/234562##### Utgivelsesdato

2011##### Metadata

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##### Sammendrag

This thesis focuses on energy recovery system design and energy integration to improve the energy efficiency of process plants. The objectives of this work are to (a) develop a systematic methodology based on thermodynamic principles to integrate energy intensive processes and (b) develop a mathematical programming based approach using thermodynamics and insight for solving industrial sized HENS problems.
A novel energy integration methodology, Energy Level Composite Curves (ELCC), has been developed that is a synergy of Exergy Analysis and Composite Curves. ELCC is a graphical tool which provides the engineer with insights on energy integration and this work represents the first methodological attempt to represent thermal, mechanical and chemical energy in a graphical form similar to composite curves for the thermal integration of energy intensive processes. This method provides physical insight to integrate energy sources with sinks. The methodology is useful as a screening tool, functioning as an idea generator prior to the heat and power integration step. A simple energy targeting algorithm is developed to obtain utility targets. The ELCC was applied to a methanol plant to show the efficacy of the methodology.
The Sequential Framework, an iterative and sequential methodology for Heat Exchanger Network Synthesis (HENS), is presented in this thesis. The main objective of the Sequential Framework is to solve industrial size problems. The subtasks of the design process are solved sequentially using Mathematical Programming. There are two main advantages of the methodology. First, the design procedure is, to a large extent, automated while keeping significant user interaction. Second, the subtasks of the framework (MILP and NLP problems) are much easier to solve numerically than the MINLP models that have been suggested for HENS. Application of the Sequential Framework to literature examples showed that the methodology generated solutions with total annualized costs lower than those presented in the literature. The examples showed the efficiency of the Sequential Framework in that even though there a four nested loops in the framework, the “best” solution is reached within a few iterations. This is primarily due to the capability of the stream match generator to identify superior Heat Load Distributions (HLDs) leading to low total heat transfer area and low Total Annualized Cost.
The three sub-problems in the Sequential Framework, minimum number of units (MILP model), stream match generator (“vertical” MILP model) and network generation and optimization (NLP model), are described with details on their formulation. In the minimum number of units sub-problem, it is shown that stream supply temperature are sufficient to define temperature intervals. The importance and role of Exchanger Minimum Approach Temperature (EMAT) in the stream match generator model is shown and motivated the addition of an EMAT loop in the Sequential Framework.
One of the limiting factors in the methodology is related to the computational complexity of the two MILP sub-problems where significant improvements are required to prevent combinatorial explosion. To ease this problem for the minimum number of units MILP sub-problem, it is modified to reduce the gap using physical insights and heuristics. Another novel approach tested was to reformulate some parts of the model by use of some ideas from set partitioning problems. Results show that even though both methods succeed in tightening the LP relaxation, the model solution times remain too long to overcome the size in the Sequential Framework. A problem difficulty indicator is explored to identify computationally expensive problems prior to solution. For the stream match generator MILP sub-problem, the model is modified to reduce the gap using physical insights. The objective is changed to include binary variables and priorities were set for these variables. Though these modifications showed improvement in solution time, orders of magnitude improvement are required to solve large models. Another limiting factor in the methodology is that the network generation and optimization sub-problem is formulated as a non-convex NLP leading to local optima. Clever starting value generators based on physical insight were developed to mitigate this issue.