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Planning models for industrial water systems are needed to address future environmental regulations, increasing costs of freshwater, variability on the quality of the available freshwater source, and bottlenecks caused by expansion of the capacity plant, among other reasons. In this paper, we present a method to address an increase in plant capacity associated with new water-using units planned to be added through time and/or an increase in the mass load of existing water-using units. We compare this method with two ad-hoc alternatives, which exhibit good performance for the examples shown. [ABSTRACT FROM AUTHOR]

Industrial sustainability is a vital issue in pursuing the long-term development of industrial systems. This paper utilizes the existing Ecological Input−Output Analysis (EIOA) method, in combination with known and established environmental (mass intensity) and economic (gross profit) sustainability metrics, to (i) create a systematic analysis methodology capable of evaluating various decisions, which are made by individual plant management for the benefit of their own company, and (ii) determine which option results in the best route for improving sustainable development, at the plant, industry, and regional levels. Such information will be valuable for the synergistic sustainability improvements of both individual entities as well as industrial regions. To demonstrate the efficacy of this methodology, a case study involving a network of different industries is presented. Sustainability of the base case and two modified cases is assessed. The one with the highest positive environmental and economic sustainability impact is recommended. [ABSTRACT FROM AUTHOR]

The petroleum refining industry plays a very important role in international economics and in our daily life. The world refining capacity has increased rapidly during the past decade, and this makes operation planning, scheduling, and general optimization become important tools for the refinery industry. However, environmental regulations and risks of climate change are pressuring the refinery industry to minimize its greenhouse gas emissions. In this research, a mixed-integer nonlinear programming (MINLP) model is proposed for the production planning of refinery processes to achieve maximum operational profit while reducing CO2emissions to a given target through the use of different CO2mitigation options. The options considered in this study are flow-rate balancing (decreasing the inlet flow rate to a unit that emits more CO2), fuel switching (changes in a certain operation to run with a different fuel that emits less CO2emissions, such as natural gas), and installation of a CO2capture process (e.g., the monoethanolamine (MEA) process). The objective of the MINLP model is to determine suitable CO2mitigation options for a given reduction target while meeting the demand of each final product and its quality specifications, while simultaneously maximizing profit. In this study, a global optimization algorithm is used on the different case studies considered. [ABSTRACT FROM AUTHOR]

Roaster off-gas systems play an important role in reducing SO2emissions to the atmosphere and in ensuring the satisfactory operation of nickel smelters in accordance with environmental regulations. In this paper, the dynamic models for an industrial roaster off-gas system are developed using mass continuity, momentum, and energy conservation laws. Industrial measured pressure data are compared with the steady-state pressure models that are the special case of the developed dynamic models, and good agreement is obtained. On the basis of the developed dynamic models, the effects of important variables on the dynamics of the off-gas system are studied via simulations. The developed dynamic models provide a necessary basis for high performance control development for roaster off-gas systems. [ABSTRACT FROM AUTHOR]