Ecological coal mining based dynamic equilibrium strategy to reduce pollution emissions and energy consumption

Accepted Manuscript Ecological coal mining based dynamic equilibrium strategy to reduce pollution emissions and energy consumption Jiuping Xu, Ning Ma, Heping Xie PII:

S0959-6526(17)31833-4

DOI:

10.1016/j.jclepro.2017.08.115

Reference:

JCLP 10373

To appear in:

Journal of Cleaner Production

Received Date: 21 August 2016 Revised Date:

31 March 2017

Accepted Date: 14 August 2017

Please cite this article as: Xu J, Ma N, Xie H, Ecological coal mining based dynamic equilibrium strategy to reduce pollution emissions and energy consumption, Journal of Cleaner Production (2017), doi: 10.1016/j.jclepro.2017.08.115. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Ecological coal mining based dynamic equilibrium strategy to reduce pollution emissions and energy consumption Jiuping Xua,b,∗, Ning Mab , Heping Xiea,∗∗ a Institute

of New Energy and Low-Carbon Technology, Sichuan University, Chengdu 610064, P. R. China Decision-Making Laboratory, Sichuan University, Chengdu 610064, P. R. China

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b Uncertainty

Abstract

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Coal mining activities have caused serious environmental problems; however, almost 30% of global primary energy demand is met using coal, making it difficult to replace in the near future. Under these circumstances, as it is not possible to completely cease coal mining activities, coal mines need to invest in cleaner production techniques to reduce environmental damage. To achieve pollution emissions and energy reductions, this paper proposes a dynamic multi-objective mixed 0-1 model based on ecological coal mining, in which environmental technological investment and production adjustments are integrated into a dynamic system to seek ecological and economic equilibrium, and emissions reductions and energy conservation are controlled using double dynamic transfer equations. Different from previous papers, this model considers the effect of production adjustments and energy conservation on the environment and the economy with the aim of achieving global optimization through integrated planning. To solve the proposed model, a hybrid algorithm with a standard and an antithetic method-based Particle Swarm Optimization (PSOs) is developed. A case study from Chaohua Colliery is presented to demonstrate the practicality and efficiency of the optimization model. The results and comparison analyses showed that as the dynamic adjustments strengthened the production and investment coordination for global optimization, integrated production and environmental investment planning provided coal mines with a superior method to solve conflicts between the ecology and the economy.

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The significant water, soil and air pollution produced as a result of coal mining activities has deepened the conflicts between the environment and human activities, constraining economic and social development (Tiwary, 2001; ?; Bian and et al., 2009; Zhengfu and et al., 2010). The main five coal producing countries: China, the U.S., India, Australia, and Indonesia: provide almost 80.5% of the worlds coal (Dudley, 2016), however, at the same time, they all suffer from coal mining related environmental problems. For example, in 2014, gangue was one of the main three industrial floor wastes in China, with coal mining and the washing industry making up 28.7% of national waste water (of Pollution Emission Control, 2016), and in South Kalimantan, almost half of all water bodies are at risk of being contaminated by coal mining waste (Greenpeace, 2016). According to a US EPA report (EPA, 2016), coal mining accounted for 8% of total global anthropogenic methane emissions in 2010, which are projected to increase by 33% by 2030. However, according to the 2016 BP statistical review of world energy, coal provides around 30% of current global primary energy needs and in 2015, was the most abundant fossil fuel (Dudley, 2016). Although coal mining produces significant environmental pollution, it is expected to remain an important energy source for many years to come. The main coal-producing countries such as China (MEP, 2015) and the United States (EPA, 2015) have promulgated special laws and regulations for the coal industry, indicating that a reduction in the environmental

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1. Introduction

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Keywords: ecological coal mining, pollution emissions reduction, energy conservation, environmental investment, production adjustment

∗ Corresponding ∗∗ Corresponding

author. Tel.:+86-28-85418191. Fax:+86-28-85415143. E-mail address: [email protected] author. E-mail address: [email protected]

Preprint submitted to Journal of Cleaner Production

August 14, 2017

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2. Literature survey

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There have been many studies that have proposed various methods or technologies to reduce coal mining pollution, such as waste backfilling technologies to treat gangue and dust (Miao et al., 2010; Huang et al., 2011; Zhang et al., 2011, 2009), biological treatment technologies for mine water (Hallberg and Johnson, 2005; Cohen, 2006; Neculita et al., 2007), tree and chemical controls for mine dust (Maiti, 1993; Ghose and Majee, 2001), and underground coal gasification to reduce pollutants at the source (Yang and et al., 2003; Khadse and et al., 2007), all of which are hard technologies designed to directly reduce or treat pollutants. There have also been several attempts to control pollutant emissions though management improvements (Hilson and Nayee, 2002; Ghose, 2003; Liu and Liu, 2010), and environmental assessment (Ghose and Majee, 2000). Similarly, energy efficiency has been improved by technological innovations (Norgate and Haque, 2010; Guo and Dong, 2007; Frasca et al., 2012) and management optimization (Worrell et al., 2001; Tanaka, 2008; Hu and Kao, 2007). The application of hard technology can have a significant effect on the ecology but requires large investment and time, and while the application of soft technology is easier to manage, the effect is auxiliary and adds little to significant environmental improvements. Therefore, there have been attempts to integrate the hard and soft technological approaches to reduce pollutant emissions. The pollution reduction capacity of coal mines has been improved using technological investment, and emissions reduction effectiveness has been optimized using management methods. Lin and et al. (2007) established a continuous time model to evaluate optimal environmental investment decisions under economic and ecological uncertainty. Higgins and et al. (2008) built a multi-objective integer programming model with ecological benefit objective for environmental investment decision-making. My˘skov´a et al. (2013) built an assessment model of the environmental and economic effects for environmental investment decision making. Lundgren (2014) developed a regression model to investigate how the environmental expenditure and investments by Swedish industrial firms have responded to climate policies. Yu and et al. (2015) proposed a dynamic programming model for environmental investment decision-making in coal mining. These studies have demonstrated that an effective combination of hard and soft technologies can lead to substantial emissions reduction. Therefore, this paper also adopts a combination of technological investment and management optimization to reduce pollution emissions, extending the results of these previous studies. Previous studies (Higgins and et al., 2008; My˘skov´a et al., 2013; Lundgren, 2014; Yu and et al., 2015) focusing on the relationships between environmental investment, economic cost, and ecological benefit have attempted to realize maximum ecological benefit at minimal economic cost and have demonstrated that economic and ecological trade-offs

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coal mining damage has become increasingly important and that ecological coal mining has become a dominant development trend. Further, as the coal industry is a high energy consumer, energy efficiency has also become an important objective for ecological coal mining (Geller et al., 2006; Nan et al., 2010). Therefore, this paper seeks to achieve both coal mining emissions reductions (ER) and energy conservation (EC). Technological and management improvements have been the main methods for emissions reductions and energy conservation. Technological improvements can have a significant effect on the ecology but require large investments in both time and money. While management improvements are easier to implement, the effect is auxiliary and adds little to the aim of significant environmental improvements. Therefore, combinatorial optimization through technological and management improvement integration has been seen as the best way to achieve maximum ecological benefit with minimal capital and time investment. As the types of pollutants produced from coal mining activities vary, focused investment is needed to improve the technology, and production optimization must be coordinated with environmental investment. Therefore, this paper adopts a combination of technological investment and management optimization to reduce overall pollution emissions and energy consumption. However, for emissions reductions and energy conservation (EREC), tradeoffs between the economic and ecological goals must be considered by balancing environmental investment and production adjustments (EIPA). As the primary objective of ecological coal mining is sustainable development, economic and ecological development equilibrium is required. As the move to ecological coal mining is a gradual process, the influence caused by the investment into EREC capacities varies. Therefore, to determine an optimal strategy for ecological coal mining, coal mines need to divide the decision period into multiple stages according to investment behavior. Based on the above discussion, a dynamic multi-objective model is built to determine the equilibrium solution for the EIPA-EREC, in which multiobjective programming is adopted to seek the economic and ecological equilibrium and dynamic programming is adopted to deal with the multiple stages.

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3. Key problem statement

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To effectively reduce both coal mining pollution emissions and energy consumption, some basic background and descriptions are introduced. In this paper, emissions reductions (ER) and energy conservation (EC) are achieved through environmental investment and production adjustments, both of which are decided on by the coal mine managers. Environmental investment in ER includes investment in technological improvement projects to reduce pollutants at the source and treatment projects to reduce pollutants after mining, in which some projects reuse the pollutants and produce economic returns. Environmental investment in EC refers to investment in projects that can improve energy efficiency during the coal mining process. As an important factor influencing ER and EC, coal production needs to be simultaneously adjusted; that is, the coal mine manager needs to consider both the economic and ecological benefits to guarantee sustainable development, as shown in Figure 1.

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must be considered when seeking to optimize environmental investment effectiveness. However, many of these studies have evaluated ecological benefit based on achieved emissions reduction, but only evaluated the economic benefit in terms of cost, which is only a part of the overall economic benefit. In this paper, to determine the equilibrium between the economy and the ecology, economic benefit is evaluated in terms of profit. Further, previous studies have only considered the influence of investment activity on the economic and ecological benefits; however, in this paper, the production plan is taken into account to develop a more comprehensive system. Therefore, to achieve coal mining emissions reductions and energy conservation, this paper considers environmental investment and production adjustments together.

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Figure 1: The relationship of ecological and economic benefit in production and environmental investment

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In this problem, determining economic and ecological equilibrium is the main difficulty. Production adjustments influence income and costs, project operations incur operating costs but can result in economic returns, and environmental project investments require capital investment; therefore, all directly influence economic benefit. Coal production produces pollution and consumes energy, ER investment reduces pollution emissions and EC investment reduces energy consumption, all of which directly influence ecological benefit. Therefore, a multi-objective model is built to determine equilibrium between the economic and ecological benefits. 3

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4. Modelling

Based on the above description, this paper adopts dynamic, multi-objective programming to address the trade-offs between the ecological and economic objectives in the production and investment planning for coal mine ER and EC. The model is built to reflect the activities of a single coal mine to achieve both ER and EC and arrive at an objective economic and ecological equilibrium.

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In addition, decision-making also needs to be dynamically considered. Because ecological coal mining is a gradual process and environmental investment needs time to show results, fluctuations and changes in the external environment and the internal condition at the coal mines all increase the difficulty of achieving an economic and ecological equilibrium strategy. The main internal factors influencing ecological benefit are the ER and EC capacities; however, as different projects have different construction periods and effects are felt after construction, they cannot be concurrently considered. The main external factors are associated with coal market fluctuations; therefore, if the production plan is not made based on these market changes, the overall economic benefit could suffer. Based on these investment and market problems, it is necessary to divide the whole period into stages to allow for a more precise determination of the ER and EC capacities to better control pollution emissions and energy consumption.

4.1. Assumptions (1) There is only one coal mine in the regional market.

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(2) Materials prices are uncertain as they are influenced by national and international markets.

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(3) Relevant policies have been determined.

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(4) Coal imports and exports are not considered.

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4.2. Notations Index: t : Decision stage index, t ∈ Θ = {1, 2, · · · , T }; i : Coal production pollutant index, i ∈ Υ = {1, 2, · · · , m}; j : Coal production energy index, j ∈ Ω = {1, 2, · · · , n}; k : ER project index, k ∈ Φi = {1, 2, · · · K2i }, in which k ∈ Φi1 = {1, 2, · · · K1i }(K1i ≤ K2i ) refers to the projects that can improve mining technology and reduce pollutant i emissions per unit of coal production, k ∈ Φi2 = {K1i + 1, K1i + 2, · · · , K2i } refers to the projects to improve the treatment or reuse technology and reduce the amount of pollutant i after mining; l : EC project index, l ∈ Ψ j = {1, 2, · · · , L j }. Decision variables: xt : Coal mine exploitation amount in stage t; yikt : If the coal mine invests in ER project k for pollution i in stage t, yikt = 1, otherwise, yikt = 0; z jlt : If the coal mine invests in EC project l for energy j in stage t, z jlt = 1, otherwise, z jlt = 0. Certain parameters: γ, ϕ : Coal mining recovery rate and raw coal loss rate; Rmax : Mineable coal reserves at the coal mine; AE , AI : Extracting and inventory capacity at the coal mine; I(t) : Coal inventory at the coal mine during stage t; drik , dc jl : Construction duration for ER projects and EC projects; ERIikd : Total investment costs when the coal mine invests in pollutant i ER project k in stage d during the construction period, d = 1, 2, · · · , drik ; ECI jld : Total investment costs when the coal mine invests in energy j EC project l in stage d during the construction period, d = 1, 2, · · · , dc jl ; 4

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: Total capital investment for all projects under construction at the the beginning of stage t; PEi , EC j : Original pollutant i emissions and energy j consumption per unit of coal output; PT ik : Improvement in pollutant i reduction after investing in ER project k; IT ikt : Actual contribution of project k to the treatment capacity of pollutant i at the beginning of stage t; ES jl : Improvement in energy j conservation per unit of coal production after investing in the EC project l; IC jlt : Actual contribution of project l to energy j conservation capacity at the beginning of stage t; AERikt : Actual ER amount of pollutant i from project k at each stage; CERi (t) : Cumulative pollutant i ER amount at the end of stage t; UEC j (t) : Energy j consumption per unit of coal production at the end of the stage t; Mt , AM(t) : Newly added and actual available capital for environmental investment at the beginning of stage t. Bikt , Cikt : Unit benefit and unit operation costs of treating pollutant i in project k; Uncertain parameters: P˜ t , C˜E jt : Prices of unit coal and unit energy j in stage t; C˜F t , C˜I t : Production cost and inventory cost per unit of coal; ˜ : Coal market minimum demand and normal demand during stage t. D˜ min t , Dt r˜t : Capital interest rate in stage t;

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4.3. Maximum economic profit The priority for market-based collieries is achieving the highest profit. Before coal mine profit can be determined, however, it is necessary to know coal production income, production costs, inventory costs, project investment costs, project operating costs, and project investment income. The influence of the production plan on profit is reflected in the production income, costs and inventory costs. Coal production income in each stage is the product of the coal unit price and the coal supply P˜ t × S t . Generally, total coal production costs can be expressed as the product of total unit costs and total coal production. However, in the EIAP-EREC, after EC project implementation, a change occurs in both the energy consumption per unit of coal output UEC j (t) and in part of the overall unit energy consumption costs. Therefore, the energy consumption cost at P each stage needs be calculated separately, which can be expressed as j∈Ω C˜ Ejt × UEC j (t) × xt , with other production P  ˜E ˜F costs being C˜ tF × xt . Therefore, the total production costs at each stage are j∈Ω C jt × UEC j (t) + C t × xt . Total inventory costs are the product of coal unit inventory costs and the actual inventory C˜I t × I(t). The influence of the investment plan on profit is reflected in the investment costs and project operating profit. ER and EC project investment requires capital, therefore, during construction, each project needs capital investment at the beginning of each stage. However, as each project has different construction durations (Altman and et al., 1996; Yu and et al., 2015), the cost cannot be simply expressed as the linear product of the variables Y, Z and the investment costs. Taking the ER project k for pollutant i as an example, for projects with construction durations of one time unit or shorter drik ≤ 1, investment is completed in the current stage, which is expressed as ERIik1 × yikt . For projects that have durations of more than one time unit, the total capital investment for project k at the beginning of stage t is the sum of the current investment of all unfinished projects. This means that during a construction period, the total investment cost in any stage is influenced by all decisions in the earlier stages. When 1 < drik ≤ t, the total investment P P cost is dd=1 rik ERIikd × yik(t−d+1) . When drik > t, the current total investment cost is td=1 ERIikd × yik(t−d+1) . The total cost for the investment in ER projects at stage t can be described by equation:

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This relationship is also suitable for EC projects. Therefore, the coal mine total investment costs at each stage are TCIt =

X X min{max{dr X ik ,1},t} i∈Υ k∈Φi

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ERIikd × yik(t−min{t,d}+1) +

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ECI jld × z jl(t−min{t,d}+1) .

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Project operations produce both costs and income. Project operating profit can be expressed as a product of the unit P P profits and the total treated pollutant quantities i∈Υ k∈Φi (Bikt − Cikt ) × AERikt . Based on the above description, and considering the time value of money, the total profit is

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    XX X    X P˜ t × S t −  C˜E jt × UEC j (t) + C˜F  × xt − C˜ I t × I(t) − TCIt + (Bikt − Cikt ) × AERikt  × (1 + r˜t )T −t , t    t∈Θ

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for which the fuzzy variables are transform into expected values using an expected value operator (Xu and Zhou, 2011).

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4.4. Maximum ecological benefit For environmental protection, coal mines wish to minimize pollutant emissions and energy consumption. However, governments in different countries have different evaluation indexes for ER and EC ecological benefits and some have multiple evaluation indexes for certain ER pollutants. Therefore, function gi (CPEi (t)) is used to express the pollutant i ER effectiveness evaluation index and function u j (UEC j (t)) is used to express the energy j consumption reduction effectiveness evaluation index. Functions gi (·) and u j (·) can be determined based on local laws and regulations. As it is difficult to achieve maximum ecological benefit for all pollutants and energy, to optimize the total ecological benefit, it is necessary to maximize the minimum ecological benefit of all ER and EC projects, as shown in Eq. (2).

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4.5. Constraints for production and investment (1) Total mining quantity restriction The total mining quantity must be no more than the mineable coal reserves at the colliery Rmax . Further, as some resources cannot be exploited due to the technical and cost restrictions in underground coal mining, raw coal losses as a result of the coal mining process cannot be neglected. The total mining restriction can be calculated as follows: P t∈Θ xt ≤ Rmax , (3) γ

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(2) Mining capacity restrictions In each decision stage, the colliery’s total mining quantity cannot exceed its mining capacity. The mining quantity is also decided on by the planned mining amount xt and is influenced by the recovery rate; so the mining capacity restriction is as follows: xt ≤ AE t ∈ Θ, γ

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(3) Social responsibility restriction As coal has a special status to ensure the safety and stability of the national economy, (Yu and Wei, 2012), coal mines need to meet minimal demand. Further, coal losses from the washing and preparation processes are inevitable. Therefore, if the coal mine wants to satisfy basic social needs, there is a social responsibility restriction, as follows: (1 − ϕ) × xt + I(t) ≥ Dmin t , t ∈ Θ. 6

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(4) Inventory capacity restrictions Coal mines produce coal and sell it on the market, with the unsold coal being stacked in a coal yard as inventory. Obviously, as the coal stacked in the coal yard cannot exceed inventory capacity; therefore: I(t) ≤ AI , t ∈ Θ.

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(5) Limited available investment capital Available investment capital at each stage limits ER and EC investment decisions as total investment cannot exceed the available capital AM(t) in the current stage. TCIt ≤ AM(t), t ∈ Θ.

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yikt ≤ 1, i ∈ Υ, k ∈ Φi1 ,

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4.6. State process control production and investment (1) State process inventory control If the available coal is less than market demand, the current inventory is zero as all available coal is sold to meet demand. Therefore, the inventory at each stage is the sum of the previous stage’s inventory and the current production minus the current coal sold to meet market demand,:

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(6) Non stacking technological investment The technological effect of ER or energy conservation at the source cannot be superimposed, which means coal mines cannot achieve higher ER or EC capacities by repeatedly investing in the same technology. Therefore, for these technologies, coal mines only need to invest one time (as Eq. (8)), otherwise, there would be waste.

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AM(1) = M1 , AM(t) = Mt + (1 + r˜t−1 ) × (AM(t − 1) − TCIt−1 ), , t ∈ Θ.

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where S t = min{D˜ t , I(t − 1) + xt } is the current coal supply for the market. (2) State process control of available investment capital Available investment capital at each stage is the sum of the actual value of the surplus capital from the previous stage in the current stage: (1 + r˜t−1 ) × (AM(t − 1) − TCIt−1 ), and the newly added capital Mt in the current stage, as in Eq. (12).

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I(t) = I(t − 1) + xt − S t , t ∈ Θ, I(0) = 0,

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(3) State process control of cumulative energy reduction Coal mines must monitor cumulative ER quantities and adjust investment and production strategies at each stage to guarantee that the ER rate for the whole period compared to previous period maintains the government standards. In this paper, state transition functions are used to describe the dynamic features in the model and state variables are used to describe the cumulative emissions reduction quantities. The ER projects 1 to K1i of pollutant i, makes a new contribution IT ikt to the ER capacity of the coal mine only when the construction is completed in stage t. Therefore, when t ≤ drik , it is impossible to complete the project k, therefore, IT ikt must be 0. When t ≥ drik , the investment decisions in stage t −drik directly influence IT ikt , yik(t−drik ) = 1 indicates that the project k can be completed in stage t, and can newly contribute to ER capacity; otherwise, there is 7

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no new contribution from project k to ER capacity in stage t. Therefore, the new contribution IT ikt of project k for pollutant i to the ER capacity should be ( PT ik × 0, if t ≤ drik , IT ikt = , i ∈ Υ, k ∈ Φi , t ∈ Θ. (14) PT ik × yik(t−drik ) , if t > drik .

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PK1i Pt Therefore, until stage t, the actual unit ER capacity is k=1 v=1 IT ikt and the treatment capacities for pollutant i PK2i Pt are k=K i +1 v=1 IT ikt . The cumulative pollutant i ER at the end of each stage CERi (t) is the sum of the cumulative 1 emissions reductions at the end of the previous stage CERi (t − 1) and the ER in the current stage, as follows:      X X  t t XX    IT ikt  × xt + IT ikt  , i ∈ Υ, t ∈ Θ (15) CERi (t) = CERi (t − 1) +    i  i Obviously, in stage 1, as the ER plan has not been implemented, and the original state is CERi (0) = 0.

(4) State process control of unit EC Energy consumption is related to efficiency; therefore, EC projects reduce energy consumption by a certain amount per unit of produced coal. The new contribution IC jlt of the projects l for energy j in stage t is ( ES jl × 0, if t ≤ dc jl , IC jlt = j ∈ Ω, l ∈ Ψ j , t ∈ Θ. (17) ES jl × z jl(t−dc jl ) , if t > dc jl .

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Therefore, in stage t, the actual unit EC capacity is the sum of unit energy conservation in the last stage and all new added capacity: X UEC j (t) = UEC j (t − 1) + IC jlt , j ∈ Ω, t ∈ Θ (18)

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4.7. Global model Based on the previous description, a multi-objective dynamic model is built to optimize the strategies for environmental investment and production adjustments to trade-off the ecological and economic benefits and achieve higher emissions and energy reductions.

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UEC j (0) = 0.

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    X   X  XX P˜ × S −  C˜E × UEC (t) + C˜F  × x − C˜I × I(t) − TCI +  × (1 + r˜ )T −t , (B − C ) × AER t jt j t t t t ikt ikt ikt    t   t  t∈Θ

i∈Υ k∈Φi

j∈Ω

o max f2 = min g1 (CPE1 (T )), · · · , gi (CPEi (T )), · · · , gt (CPEm (T )), u1 (UEC1 (t)), · · · u j (UECi (T )), · · · , un (UECn (T ))  Pt∈Θ xt  ≤ Rmax ,   γ     (1 − ϕ) × xt ≥ D˜ min  t , t∈Θ     AM(1) = M 1 ,       AM(t) = M t + (1 + rt ) × (AM(t − 1) − TCIt−1 ), t ∈ Θ    P P Pmin{max{drik ,1},t}    TCIt = i∈Υ k∈Φi d=1 ERIikd × yik(t−min{t,d}+1)    P P Pmin{max{dc jl ,1},t}    + ECI jld × z jl(t−min{t,d}+1)  j∈Ω l∈Ψ j d=1      TCI ≤ AM(t), t ∈ Θ t      I(t) = I(t − 1) + xt − S t ≤ AI , t ∈ Θ      I(0) = 0     ˜  S t = min{   ( Dt , I(t − 1) + xt }, t ∈ Θ     PT ik × 0, if t ≤ drik , s.t.  IT ikt = , i ∈ Υ, k ∈ Φi , t ∈ Θ   PT × y , ik ik(t−dr )  ik    P if t >Pdrik ,   Pt P  t   CERi (t) = CERi (t − 1) +  k∈Φi1 v=1 IT ikt × xt + k∈Φi2 v=1 IT ikt , i ∈ Υ, t ∈ Θ       CERi (0)( = 0, i ∈ Υ      ES jl × 0, if t ≤ dc jl ,    , j ∈ Ω, l ∈ Ψ j , t ∈ Θ IC jlt =   ES jl × z jl(t−dc jl ) , if t > dc jl ,   P    UEC j (t) = UEC j (t − 1) + l∈Ψ j IC jlt , j ∈ Ω, t ∈ Θ       UEC (0) = EC j , j ∈ Ω   P j  i    t∈Θ yikt ≤ 1, i ∈ Υ, k ∈ Φ1  P   j   t∈Θ z jlt ≤ 1, j ∈ Ω, l ∈ Ψ    t ≥ 0, t ∈ Θ  x     yikt , z jlt = 0 or 1, i ∈ Υ, k ∈ Φi , j ∈ Ω, l ∈ Ψ j , t ∈ Θ

(20)

M AN U

SC

RI PT

n

187

195

5. EIPA-EREC solution approach

196 197 198 199 200

201 202 203 204 205 206 207 208 209 210

TE

192 193

EP

191

5.1. General parameterization Model (20) is a universal model, with the practical problems being specified. Therefore, when model (20) is applied to a practical coal mine problem, it can be transformed into a specific model depending on the coal mine circumstances. In particular, functions gi (CPEi (t)) and u j (UEC j (t)) can be determined based on local emissions policies.

AC C

190

D

194

This model has a more comprehensive and systematic structure, in which production and environmental investment plans are coordinated to seek a global optimal solution, and dynamic adjustment is used to ensure better coordination. By considering the different environmental projects, the model has more extensive applicability and can therefore assist management select the most appropriate investment projects to ensure greater precision in the economic and ecological objectives. In addition, different project durations and phased capital investment are also considered to ensure the model is more practicable, thus allowing management to have greater control over the actual ER and EC effectiveness.

188 189

5.2. Fuzzy goals for the multi-objective model In this paper, as the economic and ecological objectives have different dimensions, they cannot be directly dealt with using weighted sum scalarization. Fuzzy goal programming (FGP) (Mohamed, 1997) can transform objectives with different dimensions into fuzzy goals and the corresponding membership functions, after which the objectives can be optimized to minimize the weighted sum of the deviational variable between the actual membership level and the aspiration membership level for each fuzzy goal. With this method, decision makers (DMs) can pursue an optimal objective value by pursuing the highest membership level for each objective, the original objective functions are equivalent to the objective functions that express the actual membership levels. As the membership levels are in the same dimension, this method can solve the weighted sum scalarization with different dimensions in model (20) This method has been previously adopted to solve MODM problems in different areas (El-Wahed and Sang, 2006; 9

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212 213 214 215

Hu et al., 2007; Baky, 2009, 2010) and has proved to be highly effective. Therefore, this paper adopts fuzzy goal programming to transform the multi-objective model into a single objective model. First, the optimal solution for each objective fξB is calculated and the DMs decided on the worst acceptable value W fξ (ξ is the objective index), from which the fuzzy goal f1 (x, y, z) & f1B and f2 (x, y, z) & f2B can be determined, with the membership functions being as in Eq. (21)   1,      fξ (x,y,z)− fξW , µξ ( fξ (x, y, z)) =   fξB − fξW     0,

if fξ (x, y, z) ≤ fξB ,

RI PT

211

if fξB < fξ (x, y, z) ≤ fξW , if fξ (x, y, z) ≥

fξW ,

The fuzzy goals are then rewritten as

217 218 219 220

where the auxiliary variables dξ− , dξ+ are the negative and positive deviation variables between the actual satisfactory level and the aspiration satisfactory level for each objective. Using Eqs. (21) and (22), the multi-objective model can be transformed into a single objective model by weighting and summing the negative deviations between the actual satisfactory level and the aspiration satisfactory level for each objective, as shown in model (23): min F =

X ξ∈Ξ

( s.t.

226 227 228 229 230 231 232 233 234 235 236 237 238

P where the auxiliary variable ωξ is the importance degree for objective ξ and ξ∈Ξ ωξ = 1; and S is the feasible region of Eq. (20). By minimizing the weighted sum of these deviations, decision makers can determine an overall satisfactory solution close to the optimal solution.

D

224 225

(23)

5.3. Standard and AM-based PSO for nonlinear dynamic model Model (20) integrates the continuous variables and 0-1 variables into the system, which increases the difficulties of finding an optimal solution. Further, as coal mine production is complex in practice, model (20) uses some nonlinear functions, making it difficult to solve using traditional exact solution methods. Therefore, a heuristic intelligent algorithm is adopted to solve this problem. Particle swarm optimization (PSO) proposed by Kenndy and Eberhart (1995), is not unduly influenced by objective function continuity as it uses primary math operators and can achieve good results, even in static, noisy, and continuously changing environments Song and Gu (2004). Since PSO can be implemented easily and effectively, it has been applied to solve real-world nonlinear problems Zhu et al. (2011); Chang and Shi (2011); Eghbal et al. (2011). Many studies have proven that both PSO and GA can obtain high quality solutions; however, the PSO has outperformed the GA in computational efficiency in nonlinear model with continuous variables (Hassan et al., 2005; Latiff et al., 2007; Duan et al., 2009; Eghbal et al., 2011). Further, Zeng and et al. (2014) proposed an antithetic method (AM)-based PSO and proved that this AM-based PSO was able to search for the same global optima as the standard GA, was faster than the standard PSO, and had a better computational stability when solving integer programming than the standard PSO and the standard GA. Therefore, to improve calculation efficiency, a combinational algorithm with a standard PSO and an antithetic method-based PSO (AM-based PSO) is proposed. The difference between the standard PSO and the AM-based PSO lies in the particle updating mechanism. In the standard PSO, formulas (24) and (25) are applied to update the position and velocity of each particle:     v j (τ + 1) = ω(τ)v j (τ) + c p r p pbest( j) − X j (τ) + cg rg gbest(τ) − X j (τ) , (24)

TE

223

µξ ( fξ (x, y, z)) + dξ− − dξ+ = 1, ξ ∈ Ξ (x, y, z) ∈ S .

EP

222

ωξ × dξ−

AC C

221

M AN U

216

(22)

SC

µξ ( fξ (x, y, z)) + dξ− − dξ+ = 1, dξ− , dξ+ ≥ 0, ∀ξ.

(21)

X j (τ + 1) = v j (τ + 1) + X j (τ),

(25) 10

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242 243 244 245 246 247

Standard PSO

Velocity example for n=2, m=2, K=2, L=2.

v2t

v1t

4.6

Probability Particle( )

Update Particle(  1)

 v( i 1) K  k 1,t 

Original elements

xt ( )

1.8

 2.7

0.34

0.43

y11t ( )

y12t ( )

y11t (  1)

y12t (  1)

3.4

 3.6

vm K  2,t

 vmK  ( j 1) L l 1,t  vm K  n L 1,t

Original elements

Antithetic elements

0.3

2.9

1.1

1.4

0.65

0.57

0.07

0.67

0.26

1

y21t ( )

y22 t ( )

z11t ( )

z12 t ( )

z21t ( )

z22t ( )

z11t (  1)

z12 t (  1)

1

 4.6

xt (  1)

vm K 1,t

Antithetic elements

M AN U

Velocity representation

AM-based PSO

RI PT

241

where τ is the current iteration number. Then the inertia weight ω(t) is used to determine the influence of the previous velocity on the new velocity. AM-based PSO updating is based on two kinds of elements: original elements (positive) and antithetic elements (non-positive). The maximum number of original elements is plus 1, and the minimum number of antithetic elements is minus 1, as shown in Figure 2. The new particle position can then be determined. Therefore, in the EIAP-EREC solution approach, the standard and AM-based PSOs are integrated to solve the mixed integer dynamic model, for which the standard PSO is used to seek the optimal solution to the production plan and the AM-based PSO is used to seek the optimal solution to the investment plan. The updating process is shown in 2.

SC

239 240

1

y21t (  1) y22 t (  1)

1

1

z21t (  1)

z22 t (  1)

Figure 2: The particle updating process of standard and AM-based PSO

251 252 253 254 255 256 257 258 259 260

261 262 263 264

D

TE

250

6. Case study

6.1. Case Description Chaohua Coal Mine is Zhengzhou Coal Industry Co., Ltd’s main mine and is one of the first hundred collieries in China. In the mining process, the main energy elements consumed are electricity, oil, and raw coal, and the main pollution emissions are gangue, waste water, coal mine methane (CMM) and fly ash, as shown in Fig. 4. In 2015, following the National 13th Five-Year Plan, the Henan Provincial government issued the 13th Five-Year Plan which included ER and EC requirements for coal mines: total pollutant emissions needed to be reduced by 5% compared to the previous period, and unit pollutant emissions and unit energy consumption needed to be reduced by 5% compared to the beginning of the period, thereby requiring Chaohua Coal Mine to invest in environmental projects. Zhengzhou Coal Industry Co., Ltd. already has advanced technologies to treat coal mine methane and sulfur and nitrogen; therefore, Chaohua Coal Mine only needs to focus on reducing the main coal mining pollutants: gangue, waste water and fly ash.

EP

249

Based on the above description, the step of the algorithm is as Figure 3.

AC C

248

6.2. Parametrization In this case, as China is still a developing country and economic development is still the first objective of enterprises, the Chaohua Coal Mine makes decisions based on a profit priority, with the ecological objectives being transformed into constraints to meet the local minimum policy, therefore model (20) applied in this case should be:

11

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Start Transform univeral model into specified model

multi-objective model ?

Solve the optimal value f* of each objective

No

Initialize particle swarm (X, Y, Z) (group size is N) Evaluate the fitness value of each particle (X, Y, Z)

Identify the membership function  ( f ( x, y, z )) of each objective

RI PT

Yes

Update the pbest and gbest of (X, Y, Z) Build the single objective model as Eq. (23)

SC

Updae the velocity of particle (X, Y, Z) by Eq. (24)

M AN U

Updae the position of X part by Eq. (25); Updae the position of Y, Z part by AM-based PSO updating mevhanism

output gbest as the optimal solution End

Yes

the fitness meets the target

No

Figure 3: The flowchart of the algorithm solving EIPA-EREC

EP

TE

D

Zhengzhou City

AC C

Henan Province

energy consumption

pollutant emission

Chaohua Colliery

electric

excavation

extraction

gangue waste water CMM fly ash

gangue CMM fly ash

oil

raw coal

underground lifting and transportation

ground transportation

stacking in coal yard

fly ash

fly ash

fly ash

Figure 4: The geographical location and mining process of Chaohua Coal Mine

12

mining area heating system

bottom ash SO2 , CO 2 , NO x

ACCEPTED MANUSCRIPT

max f1 =

X X E(P˜ t ) × S t − E(C˜tF ) × xt − E(C˜ Ejt ) × UEC j (t) × xt − E(C˜I t ) × I(t) t∈Θ

j∈Ω

m X Ki  X − TCIt + (Bikt − Cikt ) × AERikt × (1 + E(˜rt ))T −t

269 270

(26)

SC

267 268

CPEi (T ) ≥ α, i ∈ Υ gi1 (CPEi (T )) = ERRt (T ) = 1 − P t∈Θ U it CPEi (T ) ≥β i∈Υ gi2 (CPEi (T )) = ERRu (T ) = 1 − P Pt∈Θ xPt × PEi t∈Θ j∈Ω UEC j (t) × R j × xt P P ≥ ω, j ∈ Ω u j (UEC j (T )) = ECRu (T ) = 1 − j∈Ω EC j × R j t∈Θ xt (x, y, z) ∈ S

where, S is the flexible region of model (20), α and β are the lowest total pollutant and unit ER rate standards released by the government; and ω is the lowest unit EC rate standards. R j is the conversion rate for converting one unit of energy j into standard coal. ERRt (T ) is the actual total ER rate, ERRu (T ) is the actual unit ER rate and ECRu (T ) is the actual unit EC rate. Uit is the pollutant i emissions during stage t in the last period.

M AN U

265 266

k

RI PT

i

               s.t.              

6.3. Data Collection The basic Chaohua Coal Mine data shown in Tables 1 and 2 were obtained from the Zhengzhou Coal Group Company annual report. Table 1: The basic parameters of Chaohua Colliery Rmax (104 t) 10000

C max (104 t) 256

γ 0.9

274 275 276 277

278 279 280 281 282 283 284 285 286 287 288

TE

2012 244057 594409.9 86.56964

2013 242121 589694.7 89.88292

2014 248050 604135 87.986

2015 231473 563761.1 82.10596

The coal mine plans to invest in five pollutant treatment projects from 2016 to 2020 in accordance with the three kinds of pollutant emissions. The capital investments into the ER projects and EC projects at the beginning of each construction year and the completed treatment capacities for each project are respectively listed in Tables 3 and 4. Besides the detailed Chaohua Coal Mine coal seam data, some further parameters based on the coal mining industry were sourced from the Chinese Coal Industry Development Plan from the 13th Five-Year Plan (National Energy Administration, 2015) and the enterprise development plan, as shown in Table 5.

EP

273

2011 226270 551089 87.2604

AC C

271 272

D

Table 2: The last five years pollutant emissions of Chaohua Colliery year gangue (t) waste water (t) fly ash (t)

6.4. Results and Different Scenarios 6.4.1. Results Analysis Using the algorithm proposed in Section 5, the optimal production and investment planning solution for the coal mine can be determined, as shown in Table 6. The changes for each ER-EC index condition are shown in Figure 5. From Table 6, it can be seen that the coal mines respectively produce 230.64, 213.50, 164.70, 169.17 and 208.92 × 104 t of coal from the 1st year to the 5th year, and invest in gangue ER project 3, waste water ER project 1 and an electricity EC project 4 in the first year; gangue ER projects 1 and 4, and electricity EC project 1 in the 2nd year; gangue ER project 3 and electricity EC project 2 in the 3rd year; gangue ER project 3 in the 4th year; and finally the fly ash ER project 3 in the 5th year. The ER and EC statuses during the whole period are shown in Figure 5, which indicates how the strategy improves the total and unit ER rate for gangue, waste water and fly ash, as well as the unit EC rate. 13

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Table 3: The parameters of emission reduction PEi (t/t)

gangue

1210

waste water

2947

fly ash

0.4292

Project k project 1 project 2 project 3 project 1 project 2 project 3 project 4 project 1 project 2 project 3

dr (year) 0 2 1 3 1 0 1 1 0 0

ERIikd (104 CNY) d=1 d=2 1135 680 400 763 860 600 1200 630 830 750 500 690 -

d=3 350 -

PT ik (t/t) 31.6 397.25 0.00266 0.0014 -

(t) 3700 1500 4700 1340 2400 22

Table 4: The parameters for energy conservation Project l

dc (year)

raw coal

0.0056

electricity

29.3682

oil

0.2291

project 1 project 2 project 3 project 1 project 2 project 3 project 4 project 1 project 2 project 3

0 0 1 2 0 1 1 0 0 0

ECI jld (104 CNY) d=1 d=2 923 545 402 650 700 748 930 1230 860 610 928 -

Cikt (CNY/t)

0 35 50 0 0 0 0 0 0 0

0 12 31 0 1.03 0.84 0.96 0 0 8.5

ES j (t/t, KHW/t, L/t)

0.00032 0.00022 0.00018 1.5199 0.6835 0.8035 1.2024 0.0106 0.0075 0.0114

SC

EC j (t/t, KHW/t, L/t)

M AN U

Energy j

Bikt (CNY/t)

RI PT

Pollutant i

Table 5: The parameters for coal market

electricity (CNY/KHW) oil (CNY/L) C˜tI (CNY/104 t) r˜t ˜ 104 t Dmin t D˜ t 104 t

2 (487,500,509) (98, 111, 123) (487,500,509) (0.70,0.78,0.85) (2.5,3.0,3.5) (1.1,1.4,1.7) (0.017,0.021,0.023) (165,176,187) (203,215,223)

D

t

1 (510,520, 528 ) (92,103,112) (510,520, 528 ) (0.68,0.75,0.84) (2.7,3.3,3.8) (1.2,1.5,1.7) (0.023,0.027,0.030) (165,173,182) (215,226,231)

TE

T P˜t (CNY/104 t) C˜tF (CNY/104 t) C˜E raw coal (CNY/t)

3 (502,514,530) (104,117,128) (502,514,530) (0.75,0.83,0.92) (2.8,3.03,3.5) (1.3,1.6,1.7) (0.017,0.024,0.026) (163,171,182) (200,208,216)

4 (481,496,505) (109,115, 130) (481,496,505) (0.73,0.86,0.91) (2.81,3.17,3.62) (1.3,1.5,1.7) (0.016,0.019,0.022) (163,170,183) (196,205,212)

5 (496,510,523) (101,113,122) (496,510,523) (0.74,0.85,0.92) (2.9,3.37,3.72) (1.4,1.6,1.7) (0.018,0.023,0.025) (162,174,181) (193,208,219)

Table 6: The optimal solution of production and investment

Gangue

project 1 project 2 project 3 project 1 project 2 project 3 project 4 project 1 project 2 project 3 project 1 project 2 project 3 project 1 project 2 project 3 project 4 project 1 project 2 project 3

1 230.64 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

EP

T (year) X (104 t) Y

Waste water

AC C

Fly ash

Z

Raw coal

Electricity

Oil

fopt (million CNY)

289 290 291

2 213.50 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0

3 164.70 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 6695.67

4 169.17 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 208.92 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

ERRt (T )

ERRu (T )

0.0625

0.0642

0.0500

0.0516

0.0743

0.0519

ECRu (T )

0.1631

6.4.2. Sensitivity Analysis Based on the different scenarios, the optimal results for the EIPA-EREC under different circumstances are presented. 14

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The unit emisison reduction rate

-0.2 -0.3

1

2

3 Stage (one year)

4

0.06 0.04 0.02 0

5

policy standard

1

2

gangue

3 Stage (one year)

waste water

4

0.2 0.15 0.1 0.05 0

5

1

2 3 4 Energy conservation rate

5

RI PT

0 -0.1

The unit energy conservation rate

0.08

Energy conservation rate

Emission reduction rate

Emission reduction rate

The total emisison reduction rate 0.1

waste gas

energy

Figure 5: The ER and EC status at each stage when α = β = ω = 0.05

293 294 295 296

Scenario 1: Stricter total ER rate standard α Figure 6 shows the optimal results when the total ER standard changes. Comparing the optimal strategies from α = 0.05 to 0.1 and 0.15, there are obvious production cuts and the investment plan requires small adjustments, which differ depending on the increases in α. Correspondingly, the coal mine’s profits decrease from 6695.67 106 CNY to 6493.51 106 CNY and 62279.16106 CNY.

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292

  0.1,   0.05,   0.05

M AN U

  0.05,   0.05,   0.05

production

230.40

230.64

104 t

213.50

208.92

  0.15,   0.05,   0.05

217.52

192.21

182.25

169.07

164.70

164.83

ERR t (T ) ERR u (T )

 0.0626

0.0642

waste water

 0.0500

0.0516

fly ash

 0.0743

0.0519

raw coal

   ECR u (T )  = 0.1631  

electricity oil 4

5

1 3 2    profit (million CNY) = 6695.67

0

164.74

ERR t (T ) ERR u (T ) 0.1119 0.0641

0.0649

0.0515

 0.1500

0.0516

 0.1258

0.0548

 0.1772

0.0581

   ECR u (T )  = 0.1626  

4

ERR t (T ) ERR u (T )

 0.1619

 0.1000

5

1 3 2   

TE

0



D

gangue

170.13

165.76 164.78

165.11

profit (million CNY) = 6493.51

0

4

5

  ECR u (T )   = 0.1460  

1 3 2    profit (million CNY) = 6279.16

Figure 6: The optimal solution under different α

308

7. Discussion and Analysis

301 302 303 304 305 306

309 310 311

AC C

299 300

EP

307

Scenario 2: Stricter unit ER rate standard β In this scenario, the ER unit standard β was increased from 0.05 to 0.06 and 0.07 to determine the optimal results. In Figure 7, when comparing the optimal strategies from β = 0.05 to 0.06 and 0.07, the ER project investment obviously increases and is earlier, while the EC project investment is later. Production has an adjustment in time when β is high. The results show that the coal mine can achieve an actual ER rate improvement to 6% with a 4354.82 106 CNY profit, or 7% with 4100.88 106 CNY profit. Scenario 3: Stricter unit EC standard ω In this scenario, the unit EC rate ω was increased from 0.05 to 0.15 and 0.25 to determine the optimal results. The results in Figure 8 show that EC project investment obviously increases and production has an adjustment in time when ω is high. Therefore, the coal mine can improve the actual unit EC rate to 15% with 6692.88 million CNY profit or achieve a 25% EC rate with 4067.09 million CNY profit.

297 298

From the analyses in these scenarios, it can be seen that an integration of production adjustments and environmental investment can achieve ecological and economic equilibrium in the EIPA-EREC, and that dynamic coordination can promote this effectiveness. Therefore, in the following, a detailed discussion is given focused on these results. 15

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  0.05,   0.05,   0.05

production

230.41

230.59

208.92

213.50

 =0.05,  =0.07, =0.05

 =0.05,  =0.06, =0.05

230.63

104 t

220.11

225.06

210.06 197.84

164.94

169.07

171.43

ERR t (T ) ERR u (T ) 0.0642

ERR t (T ) ERR u (T ) 0.0712

gangue

 0.0626

waste water

 0.0500

0.0516

 0.0500

0.0615

fly ash

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Figure 8: The optimal solution under different ω

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7.1. Comprehensive discussion for results (1) Production and investment plans coordination can trade off the ecological and economic benefit To reach the required standards, the production and investment plans have different effects but are not wholly independent of each other. The ER total standard is mainly affected by production reductions, as shown in Figure 9(a). When α increases, a reduction in production is needed to reduce pollution emissions, and the ER project investments slightly increase, which can both improve ER capacity and reduce the pressure of the production reduction. To reach the ER unit standard β, ER project investments can play a key role, as shown in Figure 9(b). When β increases, the coal mine needs to increase ER project investment; on the other hand, increasing investment gives space for production increases. Under these circumstances, production needs to be adjusted to offset some of the losses resulting from large ER project investment. EC project investments are the only approach to meet the EC unit standard ω; however, when ω is very high, concessions need to be made in the ER project investment to release capital for the EC projects, which decreases the actual ER effectiveness (as Figure 10). EC project investment can play a significant role in decreasing costs only if there is adequate coal production; however, as pointed out, the level of production is influenced by the ER standards and capacity. Therefore, investment and production must be coordinated to reduce the pollution emissions and energy consumption and guarantee maximum profit. (2) Time adjustments can promote the coordination of the production and investment plans Coordinating production and investment across time is also necessary. As seen in scenario 1, although there are production decreases in all stages, the different reduction levels indicate that production is adjusted from the earlier

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stages to the later stages when the ER total standard increases from 0.05 to 0.15,which improves the total ER rate (see Figure 10(a)), EC project investment plan changes also need to be made to offset the losses from the reduction in total production. In Scenario 2, when the ER unit standard increases from 0.06 to 0.07, the coal mines transfer production from earlier stages to later stages (see Figure 7) to fit with the changing ER project investment plans to mitigate the greater pollutant emissions from the increase in production. EC project investments are postponed so there is capital available for the ER projects. In Scenario 3, as the EC unit standard increases from 0.15 to 0.25, some production adjustments are also made in line with the EC capacity changes to guarantee maximum benefit. Therefore, time adjustments can resolve the ecological and economic equilibrium between production and investment plans. (3) The effect of total and ER unit standard changes on production is opposite When examined under different conditions (see Figure 9(a) and (b)), it can be seen that total production is most sensitive when the ER total standard α changes (from 0.05 to 0.15), second most sensitive to the ER unit standard β (from 0.05 to 0.07), and least sensitive to the EC unit standard ω changes from 0.5 to 0.25. Figure 9 shows that a decrease in α leads to a decrease in production, while an increase in β leads to an increase in production. That is because the ER rate in total amount is determined by both production and the ER capacity, while ER rate per unit is only determined by ER project investments. Therefore, production reduction as a strategy to achieve the α is implemented. When the β increases, larger ER project investments to improve the ER capacity, and then with the

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Figure 10: The ER-EC status of coal mines under different conditions

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improvement in ER capacity, the original production plan produces less pollution so under the same α, production can have a increase. (4) High efficiency projects can effectively resolve the ecological and economic equilibrium Figure 11 shows the profits under different standards. It is obvious that profits decrease with various standards increase; however, they are different sensitivities to these changing standards. Profit is most sensitive to changes in the ER unit standard β, somewhat sensitive to changes in the ER total standard α and not very sensitive to changes in the EC unit standard ω. From the results shown in Figures 6, 7 and 8 when α increases, coal production slightly decreases, investment does not change and profit decreases moderately; when β increases, capital needs to be invested in ER projects and the profitable projects need to release some money to other projects to guarantee all pollutants can reach the ER unit standard; when ω increases, there is no obvious change in coal production or capital flows from the profitable ER projects to the profitable EC projects but there is a small reduction in profit. These results indicate that the capital used in the profitable environmental projects can solve the economic and ecological problem more effectively. However, low efficiency projects would require a longer payback period, adversely influencing coal mine profit. Therefore, coal mine profit is related to the investment project efficiency; the higher the efficiency of the projects, the greater the feedback and the higher the profit.

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7.2. Suggestions for ecological coal mining Based on the above analysis and comparisons, in the following some suggestions regarding production and environmental investment at coal mines are given. (1) Consideration of coal mine long-term interests. Environmental projects need capital investment, but not all projects can bring returns. Further, as all projects have capital recovery periods, when the capital recovery period is over, a project may not seem beneficial in the short-term. However, environmental projects improve coal mine sustainability; ER projects can improve pollution treatment capacity and allow the coal mine to produce more coal under the same policy standard and EC projects can improve efficiency, enlarging the longterm colliery profit space. From along-term development perspective, these projects also improve the overall economic benefit.Therefore, for sustainable colliery development, it is necessary for coal mines to invest in technological ER and EC.

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(2) Dynamic coordination between production and investment plans. The coal mine’s two goals of environmental protection and economic benefit can be achieved through both production and investment plans. Production and project investment are affected by different standards; however, considering only one aspect to solve problems can result in substantial losses. If one side is given the main role, the other side can have an auxiliary effect by achieving the standard or decreasing the losses. For example, to reduce pollutant emissions under the same 18

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production levels, it is better to increase production in the stages after the completion of ER investment projects, or complete the ER projects before the stages in which production is expected to be very high. To minimize economic losses, ER project investment could be arranged in the later stages; correspondingly, as a reduction in production is necessary to maintain the ER rate, more coal should be produced in those stages when the coal price is high and more investment is being put into ER projects. Therefore, by coordinating production and environmental investment, collieries can achieve ecological and economic balance. (3) Necessary government policy-driven incentives. As coal mines are profit-driven organizations, they are only concerned with economic benefits if there are no policy limits. Therefore, the government acting on behalf of the public should develop policies and regulations to control pollution emissions and promote ER in enterprises (Hilson, 2000; Jenkins and Yakovleva, 2006), especially in heavily polluting industries. Further, environmental investment can result in an increased economic burden on the coal mines. To relieve the conflict between the ecological aim and coal mine profit, governments should provide some policy or financial support to coal mines to develop environmental technologies (Zhang and Wen, 2008; Hafezalkotob et al., 2015), especially waste reuse technologies, which can result in increased economic benefits for the coal mines.

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This work is supported by State Key Development Program of (for) Basic Research of China (973 Program, Grant N0. 2011CB201200), the Funds for Creative Research Groups of China (Grant No. 50221402).

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To improve coordinated ecological and economic development in coal mining, this paper built a dynamic multiobjective mixed 0-1 model based on ecological coal mining, in which coal production adjustment, ER and EC technological investment were systematically integrated using continuous and 0-1 variables, with the ER and EC conditions being controlled using a multi-dynamic transfer equation. The results from the case study demonstrated that this coal mine dynamic equilibrium strategy model can concurrently maintain economic benefits and reduce environmental damage. Sensitivity analyses under different scenarios were conducted and different plans were provided depending on the increases in the ER and EC standards. The sensitivity analyses came to some important conclusions. First, it was found that coordinating production and investment plans can trade-off the economic and ecological benefit and second, production and investment plan adjustment over time can improve equilibrium effectiveness. Compared with previous studies, a more practical, comprehensive method for coal mining and its future development was presented. Future studies, however, need to examine the influence of coal prices when there is multi-subject competition in one market on the production and development of coal mines. How government policies or laws guide regional coal mines to achieve ecological coal mining is also an important research area for regional sustainable development.

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Highlights

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• A model for pollution emissions and energy consumption reduction of coal mining. • The tradeoffs between ecology and economy of coal mining is analyzed. • Application of the model in Chaohua Coal Mine in China under different circumstance. • The coordination of investment and production play a role in ecological coal mining.

Preprint submitted to Journal of Cleaner Production

November 11, 2016