A mixed‐integer linear programming model for methanol production from steelmaking byproduct gases is presented, considering dynamic constraints of process units. Renewable energy is used to produce hydrogen for the process via electrolysis. A case study incorporates dynamic market prices and the CO2 footprint of the electric power consumption, revealing a CO2 saving potential of 18.5 % for the scenarios and configurations studied. The results indicate that upgrading the model to a design optimization in the future will increase savings.

Methanol production is one promising way to minimize the ecological impact of the conventional steelmaking process. This synthesis needs additional hydrogen, preferably produced from a green power source. In this paper, the influence of different power supply scenarios, gas storage volumes, and hydrogen production capacities on the overall carbon saving potential – defined as carbon binding ratio – from a flexible methanol production case will be investigated. A mixed‐integer linear programming model with rolling horizon is used to calculate the optimal production plan.

This paper presents and reviews the current application fields of mathematical programming in integrated iron‐ and steelmaking research. Three different fields of mathematical programming applications are identified and discussed: unit operation optimization, by‐product gas distribution, and integrated system optimization and design. Based on current research activities in the iron and steel industry, development trends for mathematical programming in integrated iron‐ and steelmaking research, such as the cogeneration of chemicals aided by renewable energy grid integration, are presented.

Policies reasoned by global climate change and increasing commodity prices due to the international energy crisis force district heating providers to transform their assets. Pit thermal energy storage combined with solar energy can improve this transformation process. Optimal energy planning of district heating systems is often achieved by applying a linear programming model due to its fast computing. Unfortunately, depicting those systems in linear programming requires complexity reduction. We introduce a method capable of designing and operating the system with the complexity increase of considering the top and bottom temperatures of the pit thermal energy storage in linear programming. Firstly, we extract and clean data from existing sites and simulations of seasonal storages. Secondly, we develop a polynomial regression model based on the extracted data to predict the top and bottom temperatures. Lastly, we develop a mixed-integer linear programming model using the predictions and compare it to existing sites. The model uses solar thermal energy, a pit thermal energy storage, and other units to meet the demand of a district heating system. The polynomial regression results show an accuracy of up to 92 % with only a few features to base the prediction. The optimization model can design the storage and depict the correlation between decreasing specific costs and thermal losses due to an increasing volume. The control strategy of the heat pump requires further improvement.