Search

This work presents a mixed-integer linear programming (MILP) heat integration model tailored to superstructure optimization. It includes energy targeting based on a transshipment model combined with linearized heat exchanger capital costs. A variable number of heat utilities at different temperature levels can be implemented. In addition, the model facilitates the utilization of high temperature heat pumps to benefit from low exergy waste heat. The heat integration model is part of the Open sUperstrucTure moDeling and OptimizatiOn fRamework (OUTDOOR) and thus can be accessed easily using its intuitive excel-based interface. A model evaluation of the MILP shows low cost deviations of 1–14 % compared to more complex models, with fast solution times. Additionally, a practical superstructure case study is presented, where internal heat recovery reduces the external heat consumption of a power-to-methanol process by 40 %, thus underlining the relevance for adequate consideration.

The past decades have seen a diversification of the sugarcane industry with the emergence of new technology to produce bioenergy from by-product and waste process streams. Given Brazil’s ambitious goal of reducing green-house gas emissions by over 40% below 2005 levels by 2030, it is of paramount importance to develop reliable decision-making systems in order to stimulate investment in these low-carbon technologies. This paper seeks to develop a more accurate optimization model to inform risk-conscious investment decisions for bioenergy generation capacity in sugarcane mills. The main objective is for the model to enable a better understanding of how Brazilian government policies, such as the electricity price in the regulated market, may impact these investments, by taking into account the uncertainty in sugar, ethanol and spot electricity markets and the interdependency between production and investment decisions in terms of saleable product mix. The proposed methodology combines portfolio optimization theory with superstructure process modeling and it relies on simple surrogates derived from a detailed sugarcane plant simulator to retain computational tractability and enable scenario analysis. The case study of an existing sugarcane plant is used to demonstrate the methodology and illustrate how the model can assist decision-makers. In all of the scenarios assessed, the model recommends investment in extra bioelectricity capacity via the anaerobic digestion of vinasse but advises against investment in second-generation ethanol production via the hydrolysis of surplus bagasse. Furthermore, the decision to upgrade the cogeneration system with a condensation turbine is highly sensitive to the electricity price practiced in the regulated market, capacity constraints on the sugar-ethanol mix, and the accepted level of risk. Another key insight drawn from the case study is that recent market conditions have favored a production focused on the sugar business, making it challenging for policy-makers to create attractive scenarios for biofuels. Long-term electricity contracting appears to be the main hedging strategy for de-risking other products and investments in the sugarcane business, provided it is priced adequately.

The growing desire to improve resource efficiency and environmental impact of industrial processes is directly linked to optimal management of heat, mass and power flows. The concept of industrial symbiosis tackles this issue by proposing interplant heat recovery and resource transfer which can bring economical and environmental benefits to each party. A comprehensive methodology is required which can easily be incorporated in the planning of industrial clusters. Therefore, a generic hybrid mixed integer linear programming superstructure has been developed to address simultaneous heat, water, and power optimization in interplant operations. Additional concepts are included in the previously-proposed water network superstructure (Kermani et al., 2017) to account for the issues related to interplant heat and mass exchange. A cold utility superstructure is included in the water network while a steam network superstructure is modified to better represent the feedwater heaters and heat recovery opportunities. The proposed methodology is applied to an industrial case study. Results exhibit a large potential for synergies among industrial sites, even in disparate sectors, and emphasize the importance of a generic approach.

This paper introduces a novel MILP approach for the design of distillation columns sequences of zeotropic mixtures that explicitly include from conventional to fully thermally coupled sequences and divided wall columns with a single wall. The model is based on the use of two superstructure levels. In the upper level a superstructure that includes all the basic sequences of separation tasks is postulated. The lower level is an extended tree that explicitly includes different thermal states and compositions of the feed to a given separation task. In that way, it is possible to a priori optimize all the possible separation tasks involved in the superstructure. A set of logical relationships relates the feasible sequences with the optimized tasks in the extended tree resulting in a MILP to select the optimal sequence. The performance of the model in terms of robustness and computational time is illustrated with several examples. Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02).