煤炭矿区节能减排多目标优化决策研究
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摘要
煤炭资源相对丰富的赋存条件决定了其在我国工业中的主导地位,然而以煤炭为主的能源消费结构导致能源利用效率低、污染物排放多,给生态环境的保护造成了极大的威胁。如今化石能源日渐枯竭,新能源的成本高且难度大。随着国际碳税的逐渐盛行,不采取节能减排措施就会被新的贸易壁垒阻碍其经济的发展。节能减排作为实现经济发展和保护环境双赢的有效途径,不仅是我国自身可持续发展的内在要求,也是为全球减缓气候变化做出的重要贡献。
煤炭行业作为9大重点耗能产业之一,在节能减排工作中承担着重要的角色。“十一五”规划节能减排战略的实施,煤炭行业在提高能源效率和减少污染排放两方面都有很大的进展。为了达到“十二五”规划节能减排的目标,必须继续依靠先进科学技术的力量,改变以往高投入、高能耗、高排放的生产模式,提高能源利用效率,加强排放物的回收利用,发展循环经济。
煤炭矿区资源配置系统错综复杂,在制定节能减排策略时需要考虑一系列的因素,例如经济、社会、环境、技术等。从可持续发展的角度,本文同时考虑经济效益、能源效益和环境效益三个目标,试图通过多目标优化决策的方法,对能耗和排放的关键工序进行节能设备改造,同时投资一定的项目来治理或综合利用排放物,优化配置矿区的煤炭生产计划。主要研究内容如下：
(1)矿区节能减排潜力。在制定节能减排策略之前,需要首先掌握矿区目前的能源消耗结构、能源利用效率、污染物的回收和排放情况。针对“十一五”规划末的能源消耗和污染排放数据,确定投入产出指标体系,基于投入的CCR-DEA模型计算矿区的能源利用效率,评估节能减排的潜力。
(2)静态多目标优化模型。选择“十二五”整个规划期的煤炭产量、关键工序设备节能改造投资、治理或综合利用项目投资作为优化的决策变量,确定经济效益、能源效益、环境效益的表达方式,描述矿区煤炭资源储量、投资资金和工序关系等约束条件,建立煤炭矿区节能减排静态多目标优化模型。(3)静态多目标模型求解算法。与单目标优化问题不同,多目标问题的优化不仅需要区分可行解和不可行解,而且还需要在多个目标之间辨别同类解的优劣。这些问题的解决依赖于约束条件的正确处理。由于多目标问题的最优解是由多个非支配解组成的Pareto解集,故在解空间内尽可能多的搜索到非支配解是得到高质量解的保证。结合NSGA-Ⅱ和PSO,充分发挥NSGA-Ⅱ的全局搜索能力和PSO局部寻优的特性,并验证PSO-NSGA-Ⅱ算法的收敛性、覆盖性、均匀性。
(4)动态多目标优化模型。根据投资决策的时序性特点,以“十二五”规划期内的每一年作为优化对象,考虑节能减排的阶段性效果,将上一年的煤炭生产计划和投资决策作为下一年制定节能减排策略的依据,建立煤炭矿区节能减排动态多目标优化模型。
(5)动态多目标模型求解算法。动态多目标模型的优化目标和约束条件随时间推移呈现动态变化的特点,决策环境不断发生改变,这就要求算法除了在固定的进化环境内尽可能多的搜索到Pareto解外,还要能探测到进化环境的任何微小的变化,并对环境变化做出正确的响应,确定新环境的进化参数。基于这些考虑,设计混合进化算法DNSGA-Ⅱ-PSO求解动态多目标模型,对算法探测环境变化、保持种群多样性、预测环境变化三个性能进行分析。
(6)满意解的筛选。快速有效地决策是建立在少数有限的候选方案的基础之上的,所建立的多目标优化模型最终求得的是一组非劣的Pareto解集,候选方案的选取还需要对Pareto解集进行进一步筛选,这需要借助于多属性决策的方法。故运用混合聚类方法SC-MTD-GAM,在保证Pareto前端分布特性的基础上,尽量减少Pareto解集的大小,考虑决策者的偏好,选择有代表性的Pareto解作为候选方案。
基于多目标优化模型和满意解筛选方法,以典型煤炭矿区超化矿的煤炭生产为应用对象,探讨矿区为了达到“十二五”规划节能减排目标如何安排煤炭生产计划,对哪些生产设备进行节能改造,投资多少项目对污染物进行治理或综合利用。得到如下结论：
(1)通过对煤炭矿区能源消费结构和污染排放情况的分析,矿区主要消耗原煤、汽油、柴油、电力四种能源,对环境主要排放二氧化硫、矿井水、煤矸石三种污染物。运用CCR-DEA模型对矿区的能源效率和减排潜力进行评估,表明矿区当前处于最优的生产前沿,能源利用效率已达到最大化,进一步实施节能减排需要利用先进技术来提高生产效率。
(2)建立的煤炭矿区节能减排投资多目标决策模型,满足了煤炭矿区节能减排投资的决策需要。该模型以原煤产量最大、能耗和污染排放最少为目标,考虑资源、工序、资金和环保等多个约束,较好地描述了中国现阶段典型煤矿节能减排投资的决策需要。(3)提出的PSO-NSGA-Ⅱ混合多目标求解算法,比NSGA-Ⅱ算法具有更好的收敛性、覆盖性、和均匀性。针对本文建立的多日标优化模型的决策变量类型前有0-1和实数的特点,提出一种混合PSO实数编码和NSGA-Ⅱ二进制编码的求解算法。通过质心距离、覆盖性、间隔距离三个性能指标与NSGA-Ⅱ的计算结果进行对比,说明PSO-NSGA-Ⅱ发挥了PSO和NSGA-Ⅱ两种算法的组合优势,具有更好的收敛性、覆盖性、均匀性。
(4)提出的DNSGA-Ⅱ-PSO混合进化算法,比DNSGA-Ⅱ-A算法更能够探测到进化环境的任何微小的变化,保持种群多样性避免算法早熟而陷入局部最优,且能通过预测以响应环境的变化。根据动态决策的特点,本文将每个投资计划年作为决策阶段,建立含有实变量和0-1变量的动态多目标模型,提出混合算法DNSGA-Ⅱ-PSO,充分利用DNSGA-Ⅱ在全局的角度引导算法的搜索方向,PSO控制局部区域的快速寻优。此混合算法采用环境探测算子探测投资环境的任何变化,结合惯性预测、高斯分布、随机生成三种新个体产生方式完成对新环境的预测。通过对每个环境下的质心距离、覆盖性、间隔距离三个性能指标与DNSGA-Ⅱ的计算结果进行对比,说明DNSGA-Ⅱ-PSO对决策环境具有更好的探测性能和预测性。
(5)运用混合聚类方法SC-MTD-GAM根据管理者的偏好对Pareto方案集进行筛选,最终得到经济偏好型、能源节约型、协调发展型三种节能减排方案。将矿区节能减排前后的煤炭生产情况进行比较分析：对于经济偏好型投资方案,矿区在达到“十二五”规划节能减排目标的前提下,保证最大的生产能力和充分利用投资资金,最大化煤炭产量；能源节约型投资方案偏重减少能源消耗量和污染物排放量,很大程度依赖于大量缩减煤炭产量的方式实现；而协调发展型方案通过降低煤炭产量、投资节能改造工程和减排项目三种方式达到节能减排的目标。无论是哪种方案,虽然煤炭产量有暂时降低的现象,但是随着节能设备的改造和综合治理利用项目的实施,节能减排的效果不断累积,煤炭产量最终呈增加的趋势,最终超过节能减排措施前的产量,说明矿区实施节能减排是一项非常必要和长期但有价值的工作,不仅能达成节能减排的目标,还从根本上优化了矿区煤炭生产的资源配置,促进矿区向“低能耗、高效率、零污染”的绿色生产模式转型。
本文的创新点主要体现在以下三个方面：
(1)建立了煤炭矿区节能减排多目标优化模型。同时以规划期内原煤产量最大、能源消耗和污染排放最少作为优化的三个目标,考虑资源、工序、资金和环保等多个约束,分别从静态和动态两个决策视角建立多目标优化模型,该模型较好地描述了中国现阶段典型煤矿节能减排投资的决策需要：在规划期内,为了达到既定的节能减排目标,矿区选择哪些节能设备和综合治理利用项目进行投资?何时进行节能减排投资?每个项目投资多少资金?
(2)提出混合进化算法PSO-NSGA-Ⅱ和DNSGA-Ⅱ-PSO。针对建立的多目标优化模型的决策变量类型兼有0-1和实数的特点,本文提出一种混合PSO实数编码和NSGA-Ⅱ二进制编码的求解算法PSO-NSGA-Ⅱ;基于动态决策阶段性的特点,利用DNSGA-Ⅱ全局搜索性能引导解的进化方向,并通过PSO局部寻优的优点加速算法的收敛,提出DNSGA-Ⅱ-PSO。这两个混合进化算法有效地继承了NSGA和PSO二者的优点,能在解空间内尽可能大的范围内搜索到更多的非支配解,同时不断趋近真实的Pareto前端。
(3)提出混合聚类方法SC-MTD-GAM。针对多目标算法求得的Pareto解集,考虑管理者偏好,在保证Pareto前端分布形状的基础上,选择具有代表性的解点作为矿区节能减排的候选方案,最终得到经济偏好型、能源节约型、协调发展型三种决策方案,对矿区制定节能减排投资计划提供科学的参考依据。
Relative abundance of coal resources determines the occurrence condition of its dominant position in the industry in our country, however, coal-dominated energy consumption structure leads to low efficiency of energy use and more emissions, which caused great threat to the ecological environment. Now the fossil fuels are gradually drying up, and exploiting new energy is high cost and difficult. With the increasing prevalence of international carbon tax, its economy will be hinder by new trade barriers due to no taking energy conservation and emissions reduction measures. Therefore, energy conservation and emissions reduction as an effective way to achieve a win-win economic development and environmental protection, not only is our own inherent requirement of sustainable development, but also an important contribution to mitigate global climate change.
The coal industry as one of nine key energy-consuming industries, plays an important role in energy conservation and emissions reduction work. With the implementation of energy conservation and emissions reduction strategy of the "11th Five-Year Plan", the coal industry both have great progress to improve energy efficiency and reduce emissions. In order to achieve the conservation and emissions reduction goal of the "12th Five-Year Plan", we must continue to rely on the power of advanced science and technology, change the past mode of production of high input, high energy consumption and high emission, to improve energy efficiency, enhance recycling emissions, and develop circular economy.
Coal mines, complex resource allocation system, need to consider a number of factors such as economic, social, environmental and technology in the strategy of energy conservation and emissions reduction. From the perspective of sustainable development, this paper simultaneously takes into account the economic benefits, energy efficiency and environmental benefits of the three goals, trying to use energy-saving equipments, invest in certain projects to control or utilization emissions, and optimize the production plan for coal mines by the approach of multi-objective optimization decisions on key processes. The main contents of this paper are as follows:
(1) Potential of energy conservation and emissions reduction in coal mines. Before the development of energy saving strategies, you need to grasp the current situation of the energy consumption structure, energy efficiency, recovery and emissions of pollutants, using the data of energy consumption and pollution emissions at the end of the "11th Five-Year Plan", determine the input-output indexes, then calculate the energy efficiency and evaluate the potential of energy conservation and emissions reduction based on the CCR-DEA model investment.
(2) Static multi-objective optimization model. The coal production, investments on the key process energy-saving equipment and governance or utilization projects in the "12th Five-Year Plan", are selected as the decision variables needed to be optimized, then espress the economic benefits, energy efficiency and environmental benefits, describe constraints included coal reserves, investment funds and processes relations, etc.. Finally, establish the static multi-objective optimization model for energy conservation and emissions reduction of coal mines.
(3) Algorithm sovling static multi-objective model. Different from the single-objective optimization problem, multi-objective optimization problem is needed to not only distinguish feasible solutions and infeasible solutions, but also identify strengths and weaknesses among the similar solutions with multiple objectives. Solving these problems depends on the handling constraints correctly. Due to that the optimal solutions of multi-objective problem is a pareto set composed by a number of non-dominated solutions, so as much as possible non-dominated solutions searched in the solution space guarantee high-quality solution. The paper combines with NSGA-II and PSO, full play global search abilities of NSGA-II and local optimization features of PSO, and verify convergence, coverage, uniformity of the PSO-NSGA-II algorithm.
(4) Dynamic multi-objective optimization model. According to the time effect of the investment decision-making, take each year of the period of "12th Five-Year Plan" as the optimization target, considerng the stage effect of energy conservation and emissions reduction, development of energy conservation and emissions reduction strategies for the next year based on the previous year's coal production plans and investment decisions. Finally, establish the dynamic multi-objective optimization model for energy conservation and emissions reduction for coal mines.
(5) Algorithm sovling dynamic multi-objective model. Objectives and constraints of the dynamic multi-objective model dynamically changes with time, leading to the decision-making environment changes continuously, which requires that the algorithm can search the pareto solution as much as possible in a fixed evolution environment, but also can detect any small changes in the evolution environment, and make correct response to environmental changes to determine the evolutionary parameters of the new environment. Based on these considerations, there needs to design the hybrid evolutionary algorithm DNSGA-II-PSO for solving dynamic multi-objective model which can detect environmental changes, maintaining population diversity, and forecasting changes in the three environments.
(6) Selection of satisfied solutions. Rapid and effective decision-making is based on a small and limited number of candidate solutions. The multi-objective optimization model established is ultimately obtained a non-inferior pareto set, so candidate solutions also need to further selecte the pareto solution set, which requires the help of multi-attribute decision-making methods. Therefore, the use of hybrid clustering method SC-MTD-GAM, minimize the size of pareto set on the base of ensuring the distribution characteristics of pareto front, choose a representative pareto solution as a candidate solution with consideration of the decision maker's preferences.
Apply multi-objective optimization model and satisfied solution selection methods into the coal production of Chao Hua mine. And explore how to arrange coal production plan, which produce equipment for energy saving and how many investment projects to control or utilize pollutants, in order to achieve targets of energy conservation and emissions reduction in the "12th Five-Year Plan". Some conclusions are as follows:
(1) After the analysis of energy consumption structure and pollution emissions for coal mines, some results are got that coal mines mainly consume four kinds of energy included coal, gasoline, diesel, electricity and discharge three pollutants of SO2, mine water, coal gangue to the environment. Using CCR-DEA model to assess energy efficiency and emission reduction potential indicate that the coal mines are currently at the optimal front of production, that means energy efficiency has been maximized. Further implementation of energy conservation requires the use of advanced technology to improve production efficiency.
(2) Multi-objective investment decision-making models of energy conservation and emissions reduction for coal mines established in this paper meet the need of investment decisions of the energy conservation and emissions reduction. This model takes the coal production largest, energy consumption and pollution emissions minimum as objectives, with consideration of multiple constraints including resources, processes, capital and environmental protection, which better describes investment decision needs of energy conservation and emissions reduction of the typical coal mines in China at the present stage.
(3) Hybrid multi-objective algorithm PSO-NSGA-Ⅱ proposed has better convergence, coverage, and uniformity than NSGA-II algorithm. For the multi-objective optimization model established characterized by decision variables of0-1and real numbers, this paper proposes a hybrid algorithm combined real-coded PSO and binary-coded NSGA-Ⅱ. Compared with NSGA-Ⅱ through the three performance indicators of the center distance, coverage and spcing, the results indicate that PSO-NSGA-II has the advantages of two algorithms with a combination of PSO and NSGA-Ⅱ,which has better convergence, coverage, uniformity.
(4) Hybrid multi-objective algorithm DNSGA-Ⅱ-PSO proposed is more able to detect any small changes in the evolution environment, maintain population diversity, avoid the algorithm precocity trapping in local optimum, and can predict changes in the environment. According to the characteristics of dynamic decisions, this paper takes each year as a decision-making stage, then establishes a dynamic multi-objective model with a combination of real and0-1variables, and proposes the hybrid algorithm DNSGA-Ⅱ-PSO which fully uses DNSGA-Ⅱ to guide the global search direction, PSO to optimize quickly in the local area. This hybrid algorithm uses a environment detection operator to detect any changes of the investment environment, forecasts the new environment with a combination of the three methods of the inertial prediction, Gaussian distribution, and randomly generation to produce new individuals. Compared with NSGA-II through three performance indicators of the center distance, coverage and spcing, the results show that DNSGA-II-PSO has better performance of detection and predictive.
(5) Hybrid clustering method based on SC-MTD-GAM with introduction of manager preferences is uesd to selecte satisfied solutions from pareto sets, and ultimately get three types of energy conservation and emissions reduction programs included the economy preferred, energy-saving and harmonious development. The situation of coal production before and after energy conservation and emissions reduction is compared:the economic-preferred investment program maximizes coal production by means of ensuring maximum production capacity and making full use of investment funds under the premise of targets of energy conservation and emissions reduction in the "12th Five-Year Plan"; the energy-saving investment program prefers to reduce energy consumption and pollutant emissions, to a large extent which is dependent on redution of coal production; and the coordinated developmental programs achieve targets of energy conservation and emissions reduction in three ways with reducing coal production, invest energy-saving engineering and emission reduction projects. In either program, although there is a temporary reduction in coal production, but it is eventually in trend to increase and finally exceed, with the implementation of of measures energy conservation and emissions reduction and the continuous accumulation the effect of energy conservation and emissions reduction. This shows that the work of energy conservation and emissions reduction is a very necessary and long but valuable one, because of not only completing emission reduction targets, but also optimizing the allocation of resources for the coal production fundamentally, which promotes the transformation of green production model called as "low-power, high efficiency, zero emission".
Innovation of this paper is mainly reflected in the following three aspects:
(1) A multi-objective optimization model of energy conservation and emissions reduction for coal mines is established. The paper establishes two multi-objective optimization models for static and dynamic perspectives respectively, taking the coal production largest, energy consumption and pollution emissions minimum during the planning period as three objectives with a consideration of multiple constraints of resources, processes, capital and environmental protection. The modesl better describe investment decision needs of energy conservation and emissions reduction of the typical coal mines in China at the present stage:in the planning period, the coal mines select which energy-saving equipment and comprehensive control and utilization projects to invest? When to invest? How much capital for each project in order to achieve targets of energy conservation and emissions reduction?
(2) Two hybrid evolutionary algorithms of PSO-NSGA-Ⅱ and DNSGA-Ⅱ-PSO are proposed. Due to the multi-objective optimization model with the characteristics of both types of decision variables0-1and real numbers, this paper proposes a hybrid algorithm PSO-NSGA-Ⅱ combined real-coded PSO and binary-coded NSGA-Ⅱ; for the stage characteristics of the dynamic decision-making, DNSGA-Ⅱ-PSO uses DNSGA-Ⅱto guide the global evolution directions of solutions and accelerate the convergence of the algorithm by PSO. This two hybrid evolutionary algorithm effectively inherite the advantages of both NSGA and PSO, which can search more non-dominated solutions in the solution space within the scope as large as possible, while constantly approach the true pareto front.
(3) A hybrid clustering method SC-MTD-GAM is proposed. It chooses the solution as a representative candidate with consideration of decision-makers'preferences on different objectives based on ensurance to in the shape of the pareto front distribution, and finally get three types of decision-making programs included the economy preferred, energy-saving and harmonious development, which provide scientific references for the coal mines when it develops investment plans of energy conservation and emissions reduction.
煤炭行业作为9大重点耗能产业之一,在节能减排工作中承担着重要的角色。“十一五”规划节能减排战略的实施,煤炭行业在提高能源效率和减少污染排放两方面都有很大的进展。为了达到“十二五”规划节能减排的目标,必须继续依靠先进科学技术的力量,改变以往高投入、高能耗、高排放的生产模式,提高能源利用效率,加强排放物的回收利用,发展循环经济。
煤炭矿区资源配置系统错综复杂,在制定节能减排策略时需要考虑一系列的因素,例如经济、社会、环境、技术等。从可持续发展的角度,本文同时考虑经济效益、能源效益和环境效益三个目标,试图通过多目标优化决策的方法,对能耗和排放的关键工序进行节能设备改造,同时投资一定的项目来治理或综合利用排放物,优化配置矿区的煤炭生产计划。主要研究内容如下：
(1)矿区节能减排潜力。在制定节能减排策略之前,需要首先掌握矿区目前的能源消耗结构、能源利用效率、污染物的回收和排放情况。针对“十一五”规划末的能源消耗和污染排放数据,确定投入产出指标体系,基于投入的CCR-DEA模型计算矿区的能源利用效率,评估节能减排的潜力。
(2)静态多目标优化模型。选择“十二五”整个规划期的煤炭产量、关键工序设备节能改造投资、治理或综合利用项目投资作为优化的决策变量,确定经济效益、能源效益、环境效益的表达方式,描述矿区煤炭资源储量、投资资金和工序关系等约束条件,建立煤炭矿区节能减排静态多目标优化模型。(3)静态多目标模型求解算法。与单目标优化问题不同,多目标问题的优化不仅需要区分可行解和不可行解,而且还需要在多个目标之间辨别同类解的优劣。这些问题的解决依赖于约束条件的正确处理。由于多目标问题的最优解是由多个非支配解组成的Pareto解集,故在解空间内尽可能多的搜索到非支配解是得到高质量解的保证。结合NSGA-Ⅱ和PSO,充分发挥NSGA-Ⅱ的全局搜索能力和PSO局部寻优的特性,并验证PSO-NSGA-Ⅱ算法的收敛性、覆盖性、均匀性。
(4)动态多目标优化模型。根据投资决策的时序性特点,以“十二五”规划期内的每一年作为优化对象,考虑节能减排的阶段性效果,将上一年的煤炭生产计划和投资决策作为下一年制定节能减排策略的依据,建立煤炭矿区节能减排动态多目标优化模型。
(5)动态多目标模型求解算法。动态多目标模型的优化目标和约束条件随时间推移呈现动态变化的特点,决策环境不断发生改变,这就要求算法除了在固定的进化环境内尽可能多的搜索到Pareto解外,还要能探测到进化环境的任何微小的变化,并对环境变化做出正确的响应,确定新环境的进化参数。基于这些考虑,设计混合进化算法DNSGA-Ⅱ-PSO求解动态多目标模型,对算法探测环境变化、保持种群多样性、预测环境变化三个性能进行分析。
(6)满意解的筛选。快速有效地决策是建立在少数有限的候选方案的基础之上的,所建立的多目标优化模型最终求得的是一组非劣的Pareto解集,候选方案的选取还需要对Pareto解集进行进一步筛选,这需要借助于多属性决策的方法。故运用混合聚类方法SC-MTD-GAM,在保证Pareto前端分布特性的基础上,尽量减少Pareto解集的大小,考虑决策者的偏好,选择有代表性的Pareto解作为候选方案。
基于多目标优化模型和满意解筛选方法,以典型煤炭矿区超化矿的煤炭生产为应用对象,探讨矿区为了达到“十二五”规划节能减排目标如何安排煤炭生产计划,对哪些生产设备进行节能改造,投资多少项目对污染物进行治理或综合利用。得到如下结论：
(1)通过对煤炭矿区能源消费结构和污染排放情况的分析,矿区主要消耗原煤、汽油、柴油、电力四种能源,对环境主要排放二氧化硫、矿井水、煤矸石三种污染物。运用CCR-DEA模型对矿区的能源效率和减排潜力进行评估,表明矿区当前处于最优的生产前沿,能源利用效率已达到最大化,进一步实施节能减排需要利用先进技术来提高生产效率。
(2)建立的煤炭矿区节能减排投资多目标决策模型,满足了煤炭矿区节能减排投资的决策需要。该模型以原煤产量最大、能耗和污染排放最少为目标,考虑资源、工序、资金和环保等多个约束,较好地描述了中国现阶段典型煤矿节能减排投资的决策需要。(3)提出的PSO-NSGA-Ⅱ混合多目标求解算法,比NSGA-Ⅱ算法具有更好的收敛性、覆盖性、和均匀性。针对本文建立的多日标优化模型的决策变量类型前有0-1和实数的特点,提出一种混合PSO实数编码和NSGA-Ⅱ二进制编码的求解算法。通过质心距离、覆盖性、间隔距离三个性能指标与NSGA-Ⅱ的计算结果进行对比,说明PSO-NSGA-Ⅱ发挥了PSO和NSGA-Ⅱ两种算法的组合优势,具有更好的收敛性、覆盖性、均匀性。
(4)提出的DNSGA-Ⅱ-PSO混合进化算法,比DNSGA-Ⅱ-A算法更能够探测到进化环境的任何微小的变化,保持种群多样性避免算法早熟而陷入局部最优,且能通过预测以响应环境的变化。根据动态决策的特点,本文将每个投资计划年作为决策阶段,建立含有实变量和0-1变量的动态多目标模型,提出混合算法DNSGA-Ⅱ-PSO,充分利用DNSGA-Ⅱ在全局的角度引导算法的搜索方向,PSO控制局部区域的快速寻优。此混合算法采用环境探测算子探测投资环境的任何变化,结合惯性预测、高斯分布、随机生成三种新个体产生方式完成对新环境的预测。通过对每个环境下的质心距离、覆盖性、间隔距离三个性能指标与DNSGA-Ⅱ的计算结果进行对比,说明DNSGA-Ⅱ-PSO对决策环境具有更好的探测性能和预测性。
(5)运用混合聚类方法SC-MTD-GAM根据管理者的偏好对Pareto方案集进行筛选,最终得到经济偏好型、能源节约型、协调发展型三种节能减排方案。将矿区节能减排前后的煤炭生产情况进行比较分析：对于经济偏好型投资方案,矿区在达到“十二五”规划节能减排目标的前提下,保证最大的生产能力和充分利用投资资金,最大化煤炭产量；能源节约型投资方案偏重减少能源消耗量和污染物排放量,很大程度依赖于大量缩减煤炭产量的方式实现；而协调发展型方案通过降低煤炭产量、投资节能改造工程和减排项目三种方式达到节能减排的目标。无论是哪种方案,虽然煤炭产量有暂时降低的现象,但是随着节能设备的改造和综合治理利用项目的实施,节能减排的效果不断累积,煤炭产量最终呈增加的趋势,最终超过节能减排措施前的产量,说明矿区实施节能减排是一项非常必要和长期但有价值的工作,不仅能达成节能减排的目标,还从根本上优化了矿区煤炭生产的资源配置,促进矿区向“低能耗、高效率、零污染”的绿色生产模式转型。
本文的创新点主要体现在以下三个方面：
(1)建立了煤炭矿区节能减排多目标优化模型。同时以规划期内原煤产量最大、能源消耗和污染排放最少作为优化的三个目标,考虑资源、工序、资金和环保等多个约束,分别从静态和动态两个决策视角建立多目标优化模型,该模型较好地描述了中国现阶段典型煤矿节能减排投资的决策需要：在规划期内,为了达到既定的节能减排目标,矿区选择哪些节能设备和综合治理利用项目进行投资?何时进行节能减排投资?每个项目投资多少资金?
(2)提出混合进化算法PSO-NSGA-Ⅱ和DNSGA-Ⅱ-PSO。针对建立的多目标优化模型的决策变量类型兼有0-1和实数的特点,本文提出一种混合PSO实数编码和NSGA-Ⅱ二进制编码的求解算法PSO-NSGA-Ⅱ;基于动态决策阶段性的特点,利用DNSGA-Ⅱ全局搜索性能引导解的进化方向,并通过PSO局部寻优的优点加速算法的收敛,提出DNSGA-Ⅱ-PSO。这两个混合进化算法有效地继承了NSGA和PSO二者的优点,能在解空间内尽可能大的范围内搜索到更多的非支配解,同时不断趋近真实的Pareto前端。
(3)提出混合聚类方法SC-MTD-GAM。针对多目标算法求得的Pareto解集,考虑管理者偏好,在保证Pareto前端分布形状的基础上,选择具有代表性的解点作为矿区节能减排的候选方案,最终得到经济偏好型、能源节约型、协调发展型三种决策方案,对矿区制定节能减排投资计划提供科学的参考依据。
Relative abundance of coal resources determines the occurrence condition of its dominant position in the industry in our country, however, coal-dominated energy consumption structure leads to low efficiency of energy use and more emissions, which caused great threat to the ecological environment. Now the fossil fuels are gradually drying up, and exploiting new energy is high cost and difficult. With the increasing prevalence of international carbon tax, its economy will be hinder by new trade barriers due to no taking energy conservation and emissions reduction measures. Therefore, energy conservation and emissions reduction as an effective way to achieve a win-win economic development and environmental protection, not only is our own inherent requirement of sustainable development, but also an important contribution to mitigate global climate change.
The coal industry as one of nine key energy-consuming industries, plays an important role in energy conservation and emissions reduction work. With the implementation of energy conservation and emissions reduction strategy of the "11th Five-Year Plan", the coal industry both have great progress to improve energy efficiency and reduce emissions. In order to achieve the conservation and emissions reduction goal of the "12th Five-Year Plan", we must continue to rely on the power of advanced science and technology, change the past mode of production of high input, high energy consumption and high emission, to improve energy efficiency, enhance recycling emissions, and develop circular economy.
Coal mines, complex resource allocation system, need to consider a number of factors such as economic, social, environmental and technology in the strategy of energy conservation and emissions reduction. From the perspective of sustainable development, this paper simultaneously takes into account the economic benefits, energy efficiency and environmental benefits of the three goals, trying to use energy-saving equipments, invest in certain projects to control or utilization emissions, and optimize the production plan for coal mines by the approach of multi-objective optimization decisions on key processes. The main contents of this paper are as follows:
(1) Potential of energy conservation and emissions reduction in coal mines. Before the development of energy saving strategies, you need to grasp the current situation of the energy consumption structure, energy efficiency, recovery and emissions of pollutants, using the data of energy consumption and pollution emissions at the end of the "11th Five-Year Plan", determine the input-output indexes, then calculate the energy efficiency and evaluate the potential of energy conservation and emissions reduction based on the CCR-DEA model investment.
(2) Static multi-objective optimization model. The coal production, investments on the key process energy-saving equipment and governance or utilization projects in the "12th Five-Year Plan", are selected as the decision variables needed to be optimized, then espress the economic benefits, energy efficiency and environmental benefits, describe constraints included coal reserves, investment funds and processes relations, etc.. Finally, establish the static multi-objective optimization model for energy conservation and emissions reduction of coal mines.
(3) Algorithm sovling static multi-objective model. Different from the single-objective optimization problem, multi-objective optimization problem is needed to not only distinguish feasible solutions and infeasible solutions, but also identify strengths and weaknesses among the similar solutions with multiple objectives. Solving these problems depends on the handling constraints correctly. Due to that the optimal solutions of multi-objective problem is a pareto set composed by a number of non-dominated solutions, so as much as possible non-dominated solutions searched in the solution space guarantee high-quality solution. The paper combines with NSGA-II and PSO, full play global search abilities of NSGA-II and local optimization features of PSO, and verify convergence, coverage, uniformity of the PSO-NSGA-II algorithm.
(4) Dynamic multi-objective optimization model. According to the time effect of the investment decision-making, take each year of the period of "12th Five-Year Plan" as the optimization target, considerng the stage effect of energy conservation and emissions reduction, development of energy conservation and emissions reduction strategies for the next year based on the previous year's coal production plans and investment decisions. Finally, establish the dynamic multi-objective optimization model for energy conservation and emissions reduction for coal mines.
(5) Algorithm sovling dynamic multi-objective model. Objectives and constraints of the dynamic multi-objective model dynamically changes with time, leading to the decision-making environment changes continuously, which requires that the algorithm can search the pareto solution as much as possible in a fixed evolution environment, but also can detect any small changes in the evolution environment, and make correct response to environmental changes to determine the evolutionary parameters of the new environment. Based on these considerations, there needs to design the hybrid evolutionary algorithm DNSGA-II-PSO for solving dynamic multi-objective model which can detect environmental changes, maintaining population diversity, and forecasting changes in the three environments.
(6) Selection of satisfied solutions. Rapid and effective decision-making is based on a small and limited number of candidate solutions. The multi-objective optimization model established is ultimately obtained a non-inferior pareto set, so candidate solutions also need to further selecte the pareto solution set, which requires the help of multi-attribute decision-making methods. Therefore, the use of hybrid clustering method SC-MTD-GAM, minimize the size of pareto set on the base of ensuring the distribution characteristics of pareto front, choose a representative pareto solution as a candidate solution with consideration of the decision maker's preferences.
Apply multi-objective optimization model and satisfied solution selection methods into the coal production of Chao Hua mine. And explore how to arrange coal production plan, which produce equipment for energy saving and how many investment projects to control or utilize pollutants, in order to achieve targets of energy conservation and emissions reduction in the "12th Five-Year Plan". Some conclusions are as follows:
(1) After the analysis of energy consumption structure and pollution emissions for coal mines, some results are got that coal mines mainly consume four kinds of energy included coal, gasoline, diesel, electricity and discharge three pollutants of SO2, mine water, coal gangue to the environment. Using CCR-DEA model to assess energy efficiency and emission reduction potential indicate that the coal mines are currently at the optimal front of production, that means energy efficiency has been maximized. Further implementation of energy conservation requires the use of advanced technology to improve production efficiency.
(2) Multi-objective investment decision-making models of energy conservation and emissions reduction for coal mines established in this paper meet the need of investment decisions of the energy conservation and emissions reduction. This model takes the coal production largest, energy consumption and pollution emissions minimum as objectives, with consideration of multiple constraints including resources, processes, capital and environmental protection, which better describes investment decision needs of energy conservation and emissions reduction of the typical coal mines in China at the present stage.
(3) Hybrid multi-objective algorithm PSO-NSGA-Ⅱ proposed has better convergence, coverage, and uniformity than NSGA-II algorithm. For the multi-objective optimization model established characterized by decision variables of0-1and real numbers, this paper proposes a hybrid algorithm combined real-coded PSO and binary-coded NSGA-Ⅱ. Compared with NSGA-Ⅱ through the three performance indicators of the center distance, coverage and spcing, the results indicate that PSO-NSGA-II has the advantages of two algorithms with a combination of PSO and NSGA-Ⅱ,which has better convergence, coverage, uniformity.
(4) Hybrid multi-objective algorithm DNSGA-Ⅱ-PSO proposed is more able to detect any small changes in the evolution environment, maintain population diversity, avoid the algorithm precocity trapping in local optimum, and can predict changes in the environment. According to the characteristics of dynamic decisions, this paper takes each year as a decision-making stage, then establishes a dynamic multi-objective model with a combination of real and0-1variables, and proposes the hybrid algorithm DNSGA-Ⅱ-PSO which fully uses DNSGA-Ⅱ to guide the global search direction, PSO to optimize quickly in the local area. This hybrid algorithm uses a environment detection operator to detect any changes of the investment environment, forecasts the new environment with a combination of the three methods of the inertial prediction, Gaussian distribution, and randomly generation to produce new individuals. Compared with NSGA-II through three performance indicators of the center distance, coverage and spcing, the results show that DNSGA-II-PSO has better performance of detection and predictive.
(5) Hybrid clustering method based on SC-MTD-GAM with introduction of manager preferences is uesd to selecte satisfied solutions from pareto sets, and ultimately get three types of energy conservation and emissions reduction programs included the economy preferred, energy-saving and harmonious development. The situation of coal production before and after energy conservation and emissions reduction is compared:the economic-preferred investment program maximizes coal production by means of ensuring maximum production capacity and making full use of investment funds under the premise of targets of energy conservation and emissions reduction in the "12th Five-Year Plan"; the energy-saving investment program prefers to reduce energy consumption and pollutant emissions, to a large extent which is dependent on redution of coal production; and the coordinated developmental programs achieve targets of energy conservation and emissions reduction in three ways with reducing coal production, invest energy-saving engineering and emission reduction projects. In either program, although there is a temporary reduction in coal production, but it is eventually in trend to increase and finally exceed, with the implementation of of measures energy conservation and emissions reduction and the continuous accumulation the effect of energy conservation and emissions reduction. This shows that the work of energy conservation and emissions reduction is a very necessary and long but valuable one, because of not only completing emission reduction targets, but also optimizing the allocation of resources for the coal production fundamentally, which promotes the transformation of green production model called as "low-power, high efficiency, zero emission".
Innovation of this paper is mainly reflected in the following three aspects:
(1) A multi-objective optimization model of energy conservation and emissions reduction for coal mines is established. The paper establishes two multi-objective optimization models for static and dynamic perspectives respectively, taking the coal production largest, energy consumption and pollution emissions minimum during the planning period as three objectives with a consideration of multiple constraints of resources, processes, capital and environmental protection. The modesl better describe investment decision needs of energy conservation and emissions reduction of the typical coal mines in China at the present stage:in the planning period, the coal mines select which energy-saving equipment and comprehensive control and utilization projects to invest? When to invest? How much capital for each project in order to achieve targets of energy conservation and emissions reduction?
(2) Two hybrid evolutionary algorithms of PSO-NSGA-Ⅱ and DNSGA-Ⅱ-PSO are proposed. Due to the multi-objective optimization model with the characteristics of both types of decision variables0-1and real numbers, this paper proposes a hybrid algorithm PSO-NSGA-Ⅱ combined real-coded PSO and binary-coded NSGA-Ⅱ; for the stage characteristics of the dynamic decision-making, DNSGA-Ⅱ-PSO uses DNSGA-Ⅱto guide the global evolution directions of solutions and accelerate the convergence of the algorithm by PSO. This two hybrid evolutionary algorithm effectively inherite the advantages of both NSGA and PSO, which can search more non-dominated solutions in the solution space within the scope as large as possible, while constantly approach the true pareto front.
(3) A hybrid clustering method SC-MTD-GAM is proposed. It chooses the solution as a representative candidate with consideration of decision-makers'preferences on different objectives based on ensurance to in the shape of the pareto front distribution, and finally get three types of decision-making programs included the economy preferred, energy-saving and harmonious development, which provide scientific references for the coal mines when it develops investment plans of energy conservation and emissions reduction.
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