Elsevier

Journal of Cleaner Production

Volume 176, 1 March 2018, Pages 663-675
Journal of Cleaner Production

Multi-scale water network optimization considering simultaneous intra- and inter-plant integration in steel industry

https://doi.org/10.1016/j.jclepro.2017.12.158Get rights and content

Highlights

  • A multi-scale optimization model simultaneously considering intra- and inter-plant integration is developed.

  • Both direct and indirect integration strategies are adopted for inter-plant integration.

  • The freshwater consumption and total cost can be reduced by more than 20%.

Abstract

Chinese steel industry faces increasing pressure to reduce water consumption and wastewater impact. To perform the optimization of steel production process from the perspective of water use, this work describes a new superstructure-based water network optimization model using typical steel park as our study use. The water systems at different scales (e.g. unit-scale, plant-scale and park-scale) in the water network and their interactions are modeled for optimization. To construct the optimization model: 1) All types of unit-scale typical water systems in the park are identified, and short-cut unit models are developed as the basic elements for network superstructure; 2) Intra- and inter-plant superstructures are further established to describe potential configurations of the water network at plant-scale and park-scale respectively; 3) Built up on the element models and superstructures, a Mixed Integer Non-Linear Programming model using total annual cost as objective is set up to investigate potentials for water network optimization in a steel park. To illustrate its applicability and effectiveness, an industrial case study is carried out, in which two schemes are illustrated, one scheme (termed as scheme A) considering only indirect integration between plants, the other (termed as scheme B) considering both direct and indirect integration strategy (mixed integration). In scheme A, due to the higher water efficiency and optimal configuration in the case study, the freshwater consumption and total annual cost are reduced by 22% and 21% respectively. Scheme B shows 23% savings in total annual cost, while the reduction of freshwater consumption keeps unchanged. Besides, the information about the integration strategies of all scales (e.g. unit-, plant- and park-scale) can all be obtained, which is valuable for decision-making.

Introduction

Steel industry is known to consume large amounts of freshwater. The average freshwater consumption is 3.5 m3 for producing one-ton steel in China, whereas that value is 2.4 in developed countries. There is a great need for water conservation and wastewater reduction for Chinese steel producers. In last decade, many efforts were put on technology development and process improvement, while little effort was devoted to the integration and optimization of whole water network in steel industrial park from a system perspective. However, the regulations for steel industry have changed significantly in the past 5 years, with more stringent limit on water consumption and discharge, especially in water polluted or water-deficient area. Thus, more efficient method, especially systematic method for water conservation is desirable, as it can integrate available water saving technologies and processes to investigate potentials in water consumption reduction of the total water network, and obeys environmental regulations at the same time.

Water network optimization is a systematic approach in water conservation. The approach consists of two main categories: insight-based and programing-based methods. Insight-based method typically involves water pinch analysis. The seminal work was carried out by Wang and Smith (1994), in which flowrate targeting and design are determined using limiting composite diagram. Since then, the insight-based method was further extended by many researchers. El-Halwagi et al., 2003, Prakash and Shenoy, 2005 proposed a new material recovery pinch diagram to overcome the iterative steps in the diagram proposed before. Also, Manan et al. (2004) proposed a water cascade analysis approach to address the same issue. A thorough review of the insight-based method is presented by Foo (2009), showing that the method is widely used to provide good insights for simple and small scale cases, but it requires significant problem simplifications for complex and industrial cases. The programing-based method is originated from Takama et al., (1980), in which both water use and treatment processes were incorporated in one framework, and the optimization was formulated as a nonconvex nonlinear programming (NLP) problem. After that, the programming-based method was not studied for many years, until Doyle and Smith (1997) represented the first attempt of using sequential optimization strategy for the NLP problem. Galan and Grossmann (1999) first employed discrete variables (binary 0–1) in the formulation for distributed wastewater treatment network, giving rise to a MINLP problem. Since then many articles adopted MINLP formulation for optimization of the water network consisting water use water network and/or wastewater treatment network. Tan et al. (2009) first used programming-based method to incorporate the partitioning treatment unit model with double outlets in the water network formulation. Faria and Bagajewicz (2010) introduced the concept of complete water network that first considered pretreatment water network. Ahmetović and Grossmann (2011) proposed a generalized superstructure considering all possible alternatives, and a global optimization approach was proposed for the optimization problem based on the superstructure. A comprehensive review of programing-based water network synthesis was presented by Jezzowski (2010). Those works show that the programming-based method allows considering multiple contaminants, cost functions, and various topological constraints that may not be well involved in insight-based method, although the method is always challenged by high computational burden (Ahmetović and Grossmann, 2011). Fortunately, benefit by substantial solving methods for complex Mixed Integer Non-Linear Programming (MINLP) problems proposed in recent years (Khor et al., 2014), the computational burden will not be much high as before. Hence, programing-based method is more preferred when addressing industrial problems, as the industrial cases are typically formulated as MINLP problems.

Inter-plant integration is emphasized when addressing large-scale industrial problems in the context of Eco-Industrial Park (EIP). Water exchange is the most common type of cooperation between plants. Keckler and Allen (1998) considered water reuse in an industrial park, and the optimization was first formulated as a linear model. Nobel and Allen (2000) incorporated a geographical analysis in the optimization mentioned above. Chew et al., 2008, Chew et al., 2011 first introduced the concepts of direct and indirect integration in an EIP. In the direct integration scheme, water from a plant can be directly integrated with other plants, while in the indirect scheme, water from a plant should be sent to a utility hub before being reused by other plants. After that, Lovelady and El-Halwagi (2009) and Lovelady et al. (2009) developed a source-interception-sink superstructure, focusing on the model of the utility, which is then widely adopted when addressing industrial problems. Boix et al. (2012) proposed a multi-objective optimization strategy for the water network in an EIP based on the necessary conditions developed by Bagajewicz and Savelski (2001). Based on this work, Montastruc et al. (2013) proposed a tri-objective model in order to minimize freshwater consumption, regeneration water flowrate and number of connections. Alnouri et al. (2014) introduced pipeline merging approaches for interplant water networks. Moreover, Alnouri et al. (2015) considered both central and decentral treatment systems in the water network of an EIP. Jia et al. (2016) applied the industrial ecology in a coal chemical industry for water conservation. Pan et al. (2016) developed a four-level modeling framework for EIPs as well as optimization approaches for such multi-level system. Tiu and Cruz (2017) proposed a model considering both economic and environmental objective, in which the water volume and quality were considered. Leong et al. (2017) used the integrated analytic hierarchy process technique in the multi-objective optimization of an EIP. A recent review of the design of an EIP was given by Boix et al. (2015).

However, few works on the water network optimization appear in steel industry, partly due to the complexity of the practical optimization model for large-scale water network in a steel park. The typical long process of steel industry includes plants of raw material yard (RM), sintering (SI), coking (CO), iron-making (IM), steel-making (SM), continuous casting (CC), hot rolling (HR), cold rolling (CR), section steel (SS), wire rod (WR), as shown in Fig. 1. The water networks of the plants are interconnected through utilities, i.e. centralized wastewater treatment system (CT), and desalination system (DS). And, inside the plants, there are several typical water systems with smaller scale. Hence, the total water network of the steel park involves multi-scale water systems/networks, e.g. unit-scale typical water systems, plant- and park-scale water networks. Only a few published works considered part of the above-mentioned features. Lim and Park (2010) studied the water network consisting of the sequential plants after steel-making. And some of typical water systems and the synthesis approach between plants were not well investigated in their work. Lv et al. (2015) proposed a unified unit-scale model describing some typical water systems, but without validation and further analysis. Therefore, gaps exist between the current optimization models, synthesis approaches, and their applicability to the water network of a steel park. The gaps are summarized as below:

  • (1)

    The most common configurations of the typical water systems in steel industry were not well described, for example, the recycling pattern, side-filtration and multi-outlet of the water systems, cannot be represented by current single-input single-output unit models (Lim and Park, 2010, Lv et al., 2015).

  • (2)

    In some cases, the plant-scale water network is simplified as a water use unit in the total water network (Lim and Park, 2010), ignoring the potentials of water network optimization inside plants.

  • (3)

    In established practices, the desalination system serves as a utility connecting the plant-scale water networks in steel industry. And the system commonly consists of pretreatment steps and desalting steps following the pattern of industrial practices where vessels are attached in the fixed series with multiple outlets. Therefore, the resulting integrated model of the system has multiple outlets, introducing more potential alternative connections between plants that cannot be accommodated by current superstructures.

To fill the gap, this work describes a new superstructure-based optimization model for the water network optimization in a large steel park. Firstly, unit-scale short-cut models are developed for the typical water systems by incorporating operations of recycling, side-filtration etc. into the model formulations. Secondly, the plant-scale superstructure of the water network in a typical plant is established based on the unit models and practical connection limitations, in order to search the opportunity of plant-scale water network optimization. Lastly, based on unit models and plant-scale superstructure, the park-scale water network superstructure is constructed by direct and indirect integration strategies between plants. Therefore, a unique optimization model can be obtained, accommodating models or superstructures of the all different scale water systems appeared in practical steel park.

The outline of this paper is as follows. The problem statement is presented in section 2. The proposed unit-, plant- and park-scale models and superstructures are presented section 3. In section 4, the mathematical model for the superstructure-based water network optimization problem is established. In section 5, an industrial case study is carried out. In the end, conclusions and discussions are made in section 6.

Section snippets

Problem statement

The problems addressed in this paper can be described as follows. Given a water network of a typical steel park with long steel production process:

  • Freshwater is the only external water source.

  • RM, SI, CO, IM, SM, CC, HR, CR, WR and power plant (PP) are the major plants in the steel park. Each plant consists of several typical water systems (e.g. water use and water treatment systems), and each system should meet the requirements of water flow and concentration.

  • A CT system treats effluent from

Models/superstructures for typical water systems/networks in a steel park

To optimize the water network of a steel park, efforts are made in developing models for typical unit-scale water systems and establishing superstructures for the plant- and park-scale water networks in this section.

In steel industry, in order to recycle water to reduce water consumption, water is commonly treated to some extent (e.g. water temperature, water quality) before recycling. Then the water treatment system along with the water use system forms a typical water system with specific

Mathematical model

Based on the models and superstructures established above, the mathematical models for water network optimization of the whole park are developed in this section, which mainly consists of equations about water flow and contaminant balances around every water system in the network, constraints on maximum inlet concentration limit on each system and specific topological limitations for water network formulation. The objective is total annual cost that includes freshwater cost, treatment cost,

Case study

To illustrate applicability and effectiveness of the proposed models, the water network optimization of a typical long steel production process is presented in this section. The industrial park produces 8 million ton steel per year, consumes 3700 t/h (about 3.7 m3 freshwater per ton steel) of freshwater, and discharges 815 t/h of wastewater to the environment (about 0.8 m3 wastewater per ton steel).

The plants inside the park include RM, SI, CO, IM, SM, CC, HR, CR, SS, WR and PP. The water

Conclusions

This work addresses the problem of water network optimization in a steel park with the typical long steel production process. New models are proposed for the water network that covers three typical water system scales appeared in the park, including water use/water treatment unit, plant-scale water network, and park-scale water network. And a new optimization model for overall water network in a large steel industrial park is also proposed. The major practically driven extensions in this work

Acknowledgements

The financial support from the National Natural Science Foundation of China for joint NSFC-NSF project [Grant No. 2156112001] and Major Science and Technology Program for Water Pollution Control and Treatment of China [Grant No. 2015ZX07202-013] are gratefully acknowledged.

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