Energy Balance Evaluation in Coking Wastewater Treatment


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Energy Balance Evaluation in Coking Wastewater Treatment: Optimization and Modeling of Integrated Biological and Adsorption Treatment System Hongtao Zhou, Chaohai Wei, Fengzhen Zhang, Yun Hu, Haizhen Wu, and Andrzej Kraslawski ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b03535 • Publication Date (Web): 23 Oct 2018 Downloaded from http://pubs.acs.org on October 27, 2018

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Energy Balance Evaluation in Coking Wastewater Treatment: Optimization and Modeling of Integrated Biological and Adsorption Treatment System Hongtao Zhoua, Chaohai Weia*, Fengzhen Zhanga, Yun Hua, Haizhen Wub* and Andrzej Kraslawskic a The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, School of Environment and Energy, South China University of Technology, No. 382, Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, PR China b School of Biology and Biological Engineering, South China University of Technology, No. 382, Waihuan East Road, Higher Education Mega Center, Guangzhou 510006, PR China c School of Business and Management, Lappeenranta University of Technology, Skinnarila District, Skinnarilankatu 34, Lappeenranta 53851, Finland

*Corresponding author: Chaohai Wei, Haizhen Wu E-mail address: [email protected], [email protected] Tel./Fax: +86 20 39380588, +86 20 39380586

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Highlights 1. A novel energy-saving combined system for coking wastewater treatment is established. 2. A new methodology for energy balance evaluation of wastewater treatment is proposed. 3. Optimal operating conditions and energy saving rates are obtained in the novel system. 4. Low-cost high-performance adsorbent enables net energy gain for wastewater treatment.

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Nomenclature A2BA1: Bio-treatment Integrated Double Adsorption ABC: Activity Based Classification AC: Activated Carbon (g/L) AMR: Anaerobic Membrane Reactor AOPs: Advanced Oxidation Processes AT1: First Adsorption Tank AT2: Second Adsorption Tank BET: Brunauer-Emmett-Teller BJH: Barrett-Joyner -Halenda BOD5: Biochemical Oxygen Demand for Five Days (mg/L) CA: Combustible Adsorbent (g/L) CN-: Cyanide (mg/L) CNY: Chinese Yuan COD: Chemical Oxygen Demand (mg/L) CT1: First Collecting Tank CT2: Second Collecting Tank DO: Dissolved Oxygen (mg/L) EMMS: Energy Modeling Coupled Mathematical Solution ESR: Energy Saving Rate GHGs: Greenhouse Gases (kg CO2 eq.) GWP: Global Warming Potential HRT: Hydraulic Retention Time (h) IO: Investment Costs and Operation Expenses (kWh eq.) LINGO: Linear Interactive and General Optimizer MFC: Microbial Fuel Cell MLSS: Mixed Liquor Suspended Solids (mg/L) 3

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NH3-N: Ammonia Nitrogen (mg/L) NO3-N: Nitrate Nitrogen (mg/L) OLR: Organic Load Rate (kg COD/(m3·d)) OOCs: Optimal Operating Conditions PAHs: Polycyclic Aromatic Hydrocarbons (mg/L) PPA: Performance-Price Ratio PPAeq: Equivalent PPA Qi: Mathematical Set of the i-th Operating Condition Comprised of the Basic Variables (i.e., Raw Wastewater COD, Bio-treated Effluent COD and Activated Carbon Dosage). RALP: Resource Allocation Linear Programming S-: Sulfide (mg/L) SAC: Sludge-derived Activated Carbon SCN-: Thiocyanide (mg/L) SI: Supporting Information SRG: Short-term Recovery Growth SRT: Sludge Retention Time (day) SV30: Sludge Settled Volume Tested at 30 min SVI: Sludge Volume Index (mL/g) T: Temperature (ºC) TN: Total Nitrogen (mg/L) TOC: Total Organic Carbon (mg/L) TV: Threshold Value WWTP: Wastewater Treatment Plant

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Abstract: The main objective of this study is to develop and assess a novel wastewater treatment system for coking wastewater to enable maximization of net energy gain. Additionally, a new methodology is proposed for evaluation of energy saving. The new combined bio-treatment and adsorption system (A2BA1) is designed with two adsorption reactors placed at the input and output of the bio-treatment unit. An energy modeling coupled mathematical simulation (EMMS) methodology is proposed for assessment of the energy balance. The optimal operating conditions (OOCs) are achieved and energy saving rates (ESRs) can also be calculated in the proposed methodology. The results showed that the A2BA1-system enabled energy savings in treatment of volume loads of COD and NH3-N in the range 0.82-2.96 kg COD/(m3·d) and 0.06-0.15 kg NH3-N/(m3·d), respectively. Using the EMMS methodology, the operating condition sets of Q18(2506.9, 104.2, 0.98), Q33(3008.1, 101.1, 2.00) and Q36(2524.3, 102.6, 2.02) were found to be the OOCs, and the average value of ESRs of the three OOCs reached 48.3%. In this work, Qi stood for the mathematical set of the i-th operating condition comprised of the basic variables (i.e., raw wastewater COD (mg/L), bio-treated effluent COD (mg/L) 5

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and activated carbon dosage (g/L)). Furthermore, in order to realize net energy gain in the coking wastewater treatment, the adsorbent price of activated carbon should be 54% of the current price for the same adsorption performance. It is concluded that the A2BA1-system and EMMS methodology can help decision-makers design and optimize wastewater treatment in terms of energy saving.

Keywords:

Biological treatment; Activated carbon adsorption; Energy model; Mathematical

simulation; Wastewater treatment.

INTRODUCTION The issues of fossil-fuel depletion, energy provision, environmental pollution and water resource shortage are considered as the constraints of global sustainable development.1 Water and wastewater treatment becoming the essential elements of sustainable development. As from the previous report of Gude et al. (2015), water and wastewater treatment plants in the U.S. account for 3 to 4% (56 million MW of electricity) of total electricity utilization and around 20-40% of total energy consumption in other countries such as 25% of Australia and 35% of Brazil.2 In a conventional wastewater treatment plant, the consumption of electric energy accounts for approximately 25-40% of operating costs and the value varies from 0.3 to 2.1 kWh per cubic meter of wastewater.3 Hence, electricity consumption contributes substantially to the overall environmental performance of a wastewater treatment plant (WWTP),4 and consequently, implementation of effective energy-saving strategies is an important aspect of WWTP operations. Development of such strategies requires an energy cost evaluation methodology enabling effective assessment of mass and energy balance in WWTP processes and related cost factors, thus providing a basis for efficient reduction of energy required in WWTP 6

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operation. Coking wastewater is generated in coke production processes as a typical refractory wastewater, which include high temperature carbonation, coal gas purification and by-products recovery.5 It has high concentrations of ammonia nitrogen (NH3-N), thiocyanide (SCN-), cyanide (CN-), sulfide (S-), which are not readily disposable by anaerobic digestion.6 Thus, aerobic treatment is an obvious choice for coking wastewater; however, such treatments consume more energy than anaerobic digestion. Moreover, according to the National Bureau of Statistics of China, the electric energy consumed in wastewater treatment in China is estimated at 1.45 billion kWh for the treatment of 0.29 billion tons of coking wastewater in 2015.7 It is 2.4-16.7 times higher than that of domestic sewage, therefore, coking wastewater treatment is clearly a process with high energy consumption. Thus, a low-energy technology allowing effective removal of contaminants is needed to reduce energy costs and environmental pollution for coking wastewater treatment processes. Bio-treatment for treating both municipal wastewaters8 and industrial wastewaters9 has been widely utilized. Nevertheless, several bio-active recalcitrant pollutants including halogenated organics and long-chain hydrocarbons,10 still remain in bio-treated effluent, making the discharge of this effluent inappropriate. Therefore, efforts responsible for reducing residual pollutants in bio-treated effluent should be made by using advanced treatments. Adsorption, being an effective method for wastewater treatment, is used in a number of advanced treatment processes.11 Activated carbon (AC) is a common adsorbent applied in wastewater treatment.12 It is well-known that specific types of activated carbon can selectively adsorb certain kinds of contaminants because of their surface pore channels and surface functional groups.13 However, using activated carbon in bioreactor increases 7

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sludge production and treatment costs, since the separation of activated carbon adsorbent from sludge is an energy-intensive process.14 Concerning this problem, the authors believe that the combination of bio-treatment process with the adsorption of chemical oxygen demand (COD) from bio-treated effluent and raw wastewater is useful to fill gaps of both technologies.15 Recently, various hybrid processes have been used to ensure high-quality effluent, such as advanced oxidation processes (AOPs) integrated with biological treatment,16 and microbial fuel cells (MFCs) connected with membrane bioreactor.17 The costs of above two combined treatments are, however, considered too high for most wastewater treatment.

18, 19

Thus, the improved treatment technology in energy saving

from wastewater treatment processes should be proposed in wastewater treatment. In addition to the issue of wastewater treatment effectiveness and technological feasibility, energy efficiency is an important consideration, and different methods for wastewater treatment modeling targeted at evaluating energy efficiency and energy saving potential have been presented. 20, 21

For a particular treatment process, determination of the system boundaries and basic variables of the

energy system are two crucial issues for evaluation of energy consumption.22 Generally, the costs resulted from electric power consumption and the addition of chemicals are considered as the two major elements of the energy costs. The equivalent energy cost of greenhouse gases (GHGs) emissions and sludge production are becoming significant considerations due to strict environmental protection requirements.23 But how to consider the multi-factors comprehensively in the wastewater treatment on energy saving is still a challenge. Mathematical-based methods have been applied for assessment of energy consumption using combinations of various assumptions and experimental data. An example of such an approach was presented by Azadeh et al.,24 who developed a stochastic linear 8

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programming model with a multi-period planning framework to maximize expected profit in bio-fuel production chain design. Though the result was obtained correctly, the method was complicated. Activity based classification (ABC) is a conventional management tool based on the Pareto principle, and it is widely used as a rational and systematic support tool for decision-making.25 The underlying mechanisms of the ABC approach are weighted ranking of alternatives on the basis of their importance and divided the factors into class-A, class-B and class-C. Properly, the factors of class-A are the few but vital factors and should be controlled preferentially in the management process. While ABC is a common technique in many areas of business and management, it seems, however, that the ABC method has not been applied for optimization of operating conditions in wastewater treatment processes. The first objective of this study is to develop and assess a system enabling maximization of net energy gain in coking wastewater treatment. The second objective of this work is to present an approach for modeling of the energy balance and then assess how much energy savings gained from the proposed bio-treatment integrated double adsorption system (A2BA1).

EXPERRIMENTAL SECTION Experimental Device and Operation of the A2BA1-system. A laboratory-scale A2BA1-system was used consisting of two adsorption tanks, two collecting tanks and one combined bio-treatment unit as shown in Fig. 1. Powder activated carbon (Shanghai Jinhu Activated Carbon Co., Ltd) was selected as the combustible absorbent. The main physicochemical properties determined by Brunauer-Emmett-Teller (BET) and Barrett-Joyner -Halenda (BJH) methods are: specific surface area - 803.15 m2/g; average pore size - 2.54 nm; and total pore volume - 0.46 cm3/g. The activated carbon 9

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is first used in the bio-effluent wastewater to remove remaining contaminants. Next, the activated carbon separated from the bio-effluent is transferred into the raw wastewater to adsorb more pollutants to reduce the pollution load for bio-treatment. In this study, the adsorption tank was batch-type, the activated carbon with the adsorbed contaminants could be separated after the sedimentation and the upside wastewater flowed into the next reactors. The saturated AC is combusted in a bomb calorimeter for determination of the combustion heat of the AC itself and the adsorbed organic pollutants. After adsorption, the raw wastewater is further treated in the bio-treatment system, consisting of three bioreactors (i.e., oxic-1, hydrolytic and oxic-2 reactors) and two collecting tanks. The detailed design parameters of the combined bioreactor (O1-H-O2) are shown in Table 1. The main function of the oxic-1 reactor is removal of carbon and oxidation of ammonium. The hydrolytic bioreactor is used for hydrolysis of the residual organic pollutants using NaHCO3 after oxidation in the oxic-1 reactor, and partial de-nitrification under low dissolved oxygen (DO) concentration. In this study, NaHCO3 was used to adjust the pH to keep the reactions continued in the hydrolytic bioreactor, because acid materials generated in hydrolytic process. Exhaustive nitration and advanced organic carbon removal take place in the oxic-2 reactor.26 The collecting tanks are connected to the batch adsorption and continuous bio-treatment systems. The detailed introduction of the A2BA1-system is shown in Supporting Information (S1). One thousand liters of raw coking wastewater was collected from the regulation tank of the wastewater treatment facility of the Shaoguan Steel Company plant monthly, for a period of nine months, and transported to the site of the research, where it was stored at 4 °C. The main 10

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characteristics of the coking wastewater are shown in Table 2. The activated sludge in the laboratory bioreactor was collected from the corresponding reactive units of the bio-treatment system at the plant. 0.1 M NaHCO3 aqueous solution was prepared to ensure favorable pH for bio-organism activity. 0.01 M KH2PO4 was added to satisfy the nutrient proportion needed for microorganism proliferation as few phosphorous compounds existed in the coking wastewater. Fig. 1 shows the flow of activated carbon in the system under investigation. In each adsorption tank, the mixture was stirred for 40 min at ambient temperature (28.0±3.0 ºC). The dosages of activated carbon were changed in the different operational conditions with the range of 1.0-4.0 g/L. After adsorption by activated carbon in the tanks, the wastewater was filtered using a 0.45 μm fiberglass membrane (Millipore, HVLP-4700, USA). Next, COD and nitrides measurements were performed. The wastewater flux was calculated based on HRT and Ve of the reactors and measurements with a flowmeter (Kofloc, SUS-316, Japan). The excess sludge was partially removed once the mixed liquor suspended solids (MLSS) exceeded 6000 mg/L or sludge volume index (SVI) was higher than 120 mL/g. The amount of excess sludge discharged depended on the sludge retention time (SRT) and COD removal amount. pH was adjusted by addition of 0.1 M NaHCO3 to neutralize protons produced in the biodegradation process. Dissolved oxygen was controlled during the reaction period by aeration, which favors microbial growth and pollutant removal. The operation of the bioreactors was divided into two phases. In phase I, the mixed culture was initially acclimatized to the new conditions with an approximate nutrient source ensuring biochemical oxygen demand for five days (BOD5), nitrogen and phosphorus ratios around 100:5:1. After the adaptation phase, COD increased to that of the raw coking 11

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wastewater. The two adsorption tanks were connected to the bioreactor system in phase II. A short-term recovery growth (SRG) period for the microorganisms was required as the COD load varied significantly in the initial days after addition of fresh activated carbon. Operation cycles were operated sequentially for a period of three days each, once the inflow COD load of the biological system altered. The first two days were used to adapt the bio-treatment system to the change in operating conditions. The operation data of the third day was used in the energy modeling and analysis of the results.

Analysis Methods.

Chemical oxygen demand (COD), five-day biochemical oxygen demand

(BOD5), total organic carbon (TOC), ammonia nitrogen (NH3-N), nitrate nitrogen (NO3-N), total nitrogen (TN), thiocyanide (SCN-), cyanide (CN-), sulfide (S-), oil and grease, volatile phenolic compounds, were determined using standard methods for the examination of water and wastewater.28 And mixed liquor suspended solids (MLSS), 30-min sludge settled volume tested (SV30) and sludge volume index (SVI) were determined using approaches presented previously.29 Temperature, pH and DO were obtained from the digital display. Combustion heat was determined using an oxygen bomb calorimeter (IKA WORKS, C-6000, USA) after the samples had been treated by freezer drying (Memmert, VO-200, Germany) for 48 h. All the samples were tested in triplicate.

Methodology of Energy Saving Evaluation. Energy consumed was estimated as well as energy gained in the wastewater treatment process. The calculations were performed on the basis of the properties of the input and output materials and the reaction mechanism of the bio-treatment and adsorption processes. In order to optimize the possible net energy gain, the objective function was built based on the resource allocation linear programming (RALP) model.30 Linear interactive and 12

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general optimizer (LINGO) software and ABC method were used to determine the optimal operating conditions and to assess energy saving resulting from use of the treatment system.

Energy Cost and Energy Gain Modeling. During the wastewater treatment process, three materials are added into the treatment system: combustible adsorbent (i.e., activated carbon), chemicals (e.g., NaHCO3), and air. The energy cost related to pumping and mixing is considered first. Moreover, energy is needed also for disposal of the excess sludge and GHGs. The organic contaminants adsorbed by the CA are considered as an additional energy source. The detailed energy modeling is shown in Supporting Information (S2). And the basic variables and their value ranges used in energy modeling are shown in Table 3.

Mathematical Simulation. Linear interactive and general optimizer (LINGO) software was used to obtain the optimal operating conditions of the wastewater treatment process. The ABC method was applied to determine the optimal operating conditions. The energy saving rates were calculated on the basis of the average energy cost in the wastewater treatment. Linear interactive and general optimizer (LINGO) software was used to find the extremum of the objective function, represented by Eq. (1) under the set of the constraints Eqs. (2-4).45 𝒎𝒂𝒙 𝐸𝑛𝑒𝑡 = 𝑥1 × 𝐸𝑛𝑒𝑡,1 + 𝑥2 × 𝐸𝑛𝑒𝑡,2 + … + 𝑥𝑛 × 𝐸𝑛𝑒𝑡,𝑛

(1)

𝐿1 ≤ 𝑥1 × 𝑐11 + 𝑥2 × 𝑐12 + … + 𝑥𝑛 × 𝑐1𝑛 ≤ 𝑈1

(2)

𝐿2 ≤ 𝑥1 × 𝑐21 + 𝑥2 × 𝑐22 + … + 𝑥𝑛 × 𝑐2𝑛 ≤ 𝑈2

(3)

…… 𝐿𝑘 ≤ 𝑥1 × 𝑐𝑘1 + 𝑥2 × 𝑐𝑘2 + … + 𝑥𝑛 × 𝑐𝑘𝑛 ≤ 𝑈𝑘

(4)

where xi stands for the number of Bi, Bi represents the i-th operating condition comprising a series of 13

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basic variables. As mentioned previously, the higher the value of xi, the better are the operating conditions. Nevertheless, the number of sets of operating condition, obtained directly from the xi, is limited. A modified ABC approach was applied to create more sets of operating conditions, which were verified by the other method as introduced in Supporting Information (S2). Based on the report of Feijoo and Das,46 detailed representation of the ABC method modification were described as follows: First, the sum of xi and the single ratio 𝑅𝑖 are calculated as shown in Eq. (5). Then, the values of 𝑅𝑖 are rearranged from large to small and the new single ratio defines as 𝑅𝑅𝑖. Finally, the cumulative frequency (CFi) is calculated as given by Eq. (6) and the threshold value (TV) of class-A is determined. When the cumulative frequency surpasses the upper limit of the threshold value for the first time, the corresponding mathematical sets of operating conditions are the required values. All the details are also shown in Table 4. 𝑅𝑖 =

𝑥𝑖 (5)

𝑛 ∑𝑖 = 1𝑥𝑖 𝑖

𝐶𝐹𝑖 =

∑𝑅𝑅

(6)

𝑖

𝑖=1

In the Solution Report of LINGO, shown in Supporting Information (S3), the nonzero values of xi corresponding to Qi were considered as the optimized operating conditions. The other optimal operating conditions were obtained by applying the values of Reduced Cost of Solution Report by modified ABC method. To demonstrate the reliability of the mathematical simulation presented above, the values of net energy gain were processed by ABC method directly with different threshold value (TV) of class-A 14

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compared that of the mathematical simulation approach, was regarded as another method to identify the optimal operating conditions. The final optimal operating conditions (OOCs) were determined based on these two approaches. ESR was defined as shown in Eq. (7), where Eaverage represents the average energy cost in coking wastewater treatment and Enet is the net energy gain.

𝐸𝑆𝑅 =

|

|

𝐸𝑛𝑒𝑡 ― 𝐸𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝐸𝑎𝑣𝑒𝑟𝑎𝑔𝑒

× 100%

(7)

RESULTS AND DISCUSSION Overall Process Performance.

The A2BA1-system was operated successfully in the

laboratory for nine months. An overview of the system performance is presented in Fig. 2 giving the main parameters (i.e., COD, NH3-N, NO3-N and TN) of the influent and/or effluent. The start-up period of the A2BA1-system lasted for four weeks. The system performance was in agreement with the previous study of using aerated filter reactor for coking wastewater treatment.47 The start-up period was an adaptive process for microorganism growth. It was observed that the disposal load reached up to 2.95 kg COD/(m3·d) and COD removal rate was more than 95% in the biological system. The disposal load was in agreement with the other studies of coking wastewater treatment used aerobic technology.48, 49 Similarly, the maximal influent NH3-N was 216.4 mg/L and no NO3-N was detected in the bio-influent. The disposal load and removal rate of NH3-N reached up to 0.14 kg NH3-N/(m3·d) and 98% according to the previous works.48, 50 After the acclimatization period, the coking wastewater could be treated efficiently in the bioreactor. In order to optimize the experiment, step sizes of raw wastewater COD, bio-treated effluent COD and AC dosage were determined, which were confirmed as the three basic variables for 15

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the energy modeling. The incremental change of concentration of raw wastewater COD was set as 500.0 mg/L, the lower and upper values were 2500.0 and 5000.0 mg/L, respectively. Similarly, the incremental change of concentration of the bio-treated effluent COD was 50.0 mg/L, and the lower and upper values were 100.0 and 200.0 mg/L, respectively. The designs of COD concentration were in consistent with the data from Ma et al.51 AC dosages of 1.00, 2.00, 3.00 and 4.00 g/L were selected according to the survey by Skouteris et al.18 Thus, 72 experimental points were generated after Phase I. The values of the three basic variables are shown in Supporting Information (S4). The short-term recovery growth (SRG) was determined experimentally and its value was found to be nine days. During the next eight months, in Phase II, the system operated under COD and NH3-N load ranging from 0.82 to 2.96 kg COD/(m3·d) and from 0.06 to 0.15 kg NH3-N/(m3·d), respectively. The results were consistent with the earlier report for treating coking wastewater by a SBR reactor.52 The effluent COD and NH3-N of the bio-treated wastewater were measured as 150.0±55.0 mg/L and 2.4±0.8 mg/L, respectively. In contrast, as shown in Fig. 2(c), NO3-N increased throughout the bio-treatment process and accounted for approximately 88.3% of the total nitrides (20.2±4.3 mg/L). As illustrated in Fig. 2(b), the influent NH3-N varied little under different AC dosages. This implies that NH3-N could not be adsorbed effectively by the AC.53 Over 99% of NH3-N was removed by biotransformation in bio-treatment process.54 Moreover, the bio-influent COD presented nonlinear correlation with AC addition at the same raw wastewater COD. For instance, the total COD adsorption capacity with 4.00 g/L AC addition was 2.5-2.7 times higher than that of 1.00 g/L AC at the raw wastewater COD of 3000.0±80.0 mg/L. This difference resulted from the total amount of the adsorbed pollutants.18 In addition, the adsorption capacity of the unit mass absorbent dropped from 0.57 to 0.29 kg COD/kg AC 16

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when the dosage increased from 1.00 to 4.00 g/L. These results were in accordance with our previous research in which we used the different materials for coking wastewater adsorption.55 However, the AC cost increased several-fold with the increased dosage apparently. Therefore, the AC dosage played an important role for energy saving in wastewater treatment.

Energy Modeling and Mathematical Simulation.

As illustrated in the above

subsection, the relationship of the different parameters and determination of the basic variables are shown in Fig. 3. Based on analysis of the parameters, raw wastewater COD, bio-treated effluent COD and AC dosage were selected as the three basic variables for the energy modeling. To make the energy modeling easier, step sizes were assumed for the three basic variables based on experience and the method presented in Ren et al.56 The net energy gain can be calculated with the three basic parameters determined as shown in Supporting Information (S4). After establishment of the set of experimental data, the objective function was formulated for optimization of net energy gain. The constraints were determined by the designed step sizes given in the next subsection and the ranges of basic variables given in Supporting Information (S5). The objective function under the specified constraints was calculated using LONGO 11.0. Results are given in Appendix E. In this work, Qi stood for the mathematical set of the i-th operating condition comprised of the basic variables (i.e., raw wastewater COD, bio-treated effluent COD and activated carbon (AC) dosage).The nonzero values of xi (i.e., x18, x33 and x36) correspond to the sets of the operating conditions of Q18(2506.9, 104.2, 0.98), Q33(3008.1, 101.1, 2.00) and Q36(2524.3, 102.6, 2.02). There was a big difference (around 100%) between Q18 and Q36 in terms of the activated carbon dosages. It was caused by the fact that the costs of AC and IO of Q36 were 100% and 53% higher than 17

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that of Q18, respectively. The gains from AC and raw coking wastewater of Q36 were both around 100% higher than those of Q18. Consequently, the value of energy savings between Q36 and Q18 was less than 10%. The detailed results were shown in Supporting Information (S4). The obtained operating conditions were suggested as the optimal answers in the treatment process for energy saving. The three sets of the operating condition (Q18, Q33 and Q36) achieved from mathematical simulation were regarded as the first portion of class-A of the ABC method. In order to obtain the other optimal operating conditions, an improved ABC method was used as described previously. When the cumulative percentage of the values of Reduced Cost of Solution Report (shown in Appendix E.) exceeded the upper limit of the threshold value of the class-A (i.e., 80%) for the first time, the corresponding sets of operating conditions were considered as the second portion of class-A. As a result, x37, x55, x58, x61, x64, x67 and x70 were achieved, and the corresponding operating condition sets were Q37(5063.7, 203.7, 3.00), Q55(5042.5, 203.4, 4.01), Q58(4511.4, 205.5, 4.00), Q61(4021.8, 202.4, 4.01), Q64(3513.8, 196.8, 4.03), Q67(3010.5, 201.5, 4.01) and Q70(2535.3, 202.3, 3.98). According to the results of the first portion of class-A, we found that effluent COD and AC dosage approximately at 100.0 mg/L and 2.00 g/L, respectively, were the optimal operating conditions for raw wastewater COD in the range of 2500.0-3000.0 mg/L. If the raw wastewater COD is higher than 3000.0 mg/L, then the effluent COD and AC dosage should be controlled at around 200.0 mg/L and 3.00-4.00 g/L, respectively. As see in the second portion of class-A, the effluent COD around 200.0 mg/L and the AC dosage of 4.00 g/L were the optimal operating conditions for energy saving when treating the raw wastewater with COD from 2500.0 to 5100.0 mg/L. To meet prevailing wastewater discharge standards, the COD concentration of the treated water 18

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should be less than 100.0 mg/L.57 As seen in Fig. 4, the conditions beneficial for energy saving during the treatment process were achieved when the raw wastewater COD varied from 2500.0 to 3000.0 mg/L and the treated effluent COD was controlled at 40.0±3.0 mg/L or 72.0±2.0 mg/L. In addition, the highest value of energy saving obtained was 5.40 kWh/m3 with 2.00 g/L AC addition. The energy savings were 5.9%, 37.4% and 166.6% higher than that of the AC dosage at 1.00, 3.00 and 4.00 g/L, respectively, as depicted in Fig. 5. These results indicate that 2.00 g/L AC dosage was superior in terms of energy saving and confirm the outcome of the first portion of class-A of the results obtained using mathematical simulation. It was in accordance with the published study, where the reported 2-3 g/L of CAC was the suitable dosage to be used for the phenolic wastewater. 58 Four three-dimensional histograms of specific AC dosages (Fig.6) were used to verify the mathematical solution. In order to present the histograms in a simple way, bases of the logarithms were assumed as 10 and e (≈ 2.7183) for the value of COD concentration of raw wastewater and treated water, respectively. The ABC method was used in the data processing of net energy gain as mentioned earlier. Furthermore, it was assumed that the upper limit of the threshold value of class-A was 80% , and the corresponding operating conditions were considered as the components of class-A. It was in accordance with the previous study.59 For 1.00 g/L AC addition, the class-A included the operating condition sets Q8-Q18. This implies that the lower the raw wastewater COD was, the greater the energy saving attained. To satisfy the discharge standards, Q9, Q11, Q12, Q15 and Q18 were suggested as the optimal operating conditions. Similarly, Q27, Q29, Q30, Q32, Q33, Q35 and Q36 gave OOCs with 2.00 g/L AC addition. Using the same method, all COD concentrations of the treated water were lower than 100.0 mg/L using 3.00 g/L AC. The sets of operating conditions of class-A 19

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contained Q39, Q45 and Q47-Q54. For 4.00 g/L AC addition, the results were Q60, Q62, Q63, Q65-Q66, Q68-Q69 and Q72. It should be noted that the average value of energy saving with 4.00 g/L AC dosage was less than that of the other three AC addition dosages because the growth of the cost of AC addition was faster than the energy saving resulting from the adsorbed contaminants. This result was consisted with the previous study, which demonstrated that the adsorption capacity of pollutants in wastewater treatment decreased simultaneously with the increase of the activated carbon dosage.60 Consequently, the final optimal operating conditions were Q18(2506.9, 104.2, 0.98), Q33(3008.1, 101.1, 2.00) and Q36(2524.3, 102.6, 2.02) achieved by the two approaches. These results were in accordance with the previous studies, where reported the reasonable influent COD of coking wastewater for biological treatment was 2000.0-3000.0 mg/L.29, 61 According to our investigation and data from 36 coking wastewater plants in China, the average cost of treatment of coking wastewater is around 15.00 kWh/m3.62 Therefore, the energy saving rate of every operating condition combination could be calculated using Eq. (7). The results showed that the lower and upper limits of the ESR were 4.9% and 49.1%, respectively. The results further showed that optimization of operating conditions is important since the highest value of energy saving was more than 10 times the lowest value. These were consistent with the previous study, which reported that net energy generation was possible using the different technologies such as anaerobic digestion for CH4 production and combined heat and power.63 As illustrated in Fig. 7, most of the sets of operating conditions under AC dosage of 1.00-2.00 g/L were higher than the average ESR (i.e., 27.4%) for 72 cases. While the ESR values of 4.00 g/L AC were lower than that of other adsorbent dosages. Additionally, we assumed that the corresponding operating conditions of the top 10% of the ESR 20

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values were optimized and the lower limiting value was 43.5%. It was almost equal to fifty percent energy saving due to the installed new energy-efficient blowers in the treatment plant as presented in the report of Gude et al.2 Thus, the corresponding sets of the operating condition of the top 10% of ESR values were Q16-Q18 and Q32-Q36, and their average energy saving rate (ESR) reached 48.3%. They further demonstrated that Q18(2506.9, 104.2, 0.98), Q33(3008.1, 101.1, 2.00) and Q36(2524.3, 102.6, 2.02) were the optimal operating conditions.

Possibility of Net Energy Gain with the A2BA1-system. As discussed above, the A2BA1-system could achieve around 50% energy saving thanks to optimization of the operating conditions. Such energy efficiency is a step closer to the ultimate aim of future wastewater treatment as not only a purification process but also an energy production process. Several methods have been proposed to realize this aim, e.g., CH4 generated from anaerobic treatment.63 In the A2BA1-system, the double adsorption plays an important role in energy saving in the treatment process; therefore, we suggest that net energy gain could be achieved with a higher performance-price ratio (PPA) of the added activated carbon. PPA is a common index for material comprehensive evaluation.64 In this work, we assumed adsorption capacity and combustion enthalpy as measures of CA performance. Next, the equivalent PPA (PPAeq) was calculated using Eq. (8). Activated carbon

was

selected as the combustible absorbent (CA) and its properties were described in subchapter 2.1. The obtained equivalent PPA varied in the range of 1.65-2.41 kWh/CNY due to the variation in operating conditions. It was in accordance with the published studies of bio-char from wood pyrolysis, equaled to 1.21-2.53 kWh/CNY.65, 66 In order to offset the energy cost, the average PPAeq was increased from 21

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2.01 to 3.72 kWh/CNY. This implies that the adsorbent price should be reduced to 0.54 of the current price while simultaneously maintaining the same performance as present. Therefore, low-cost high performance adsorbents such as sludge-derived activated carbon (SAC) and bio-char may become important options to realize net energy gain in wastewater treatment. These suggestions were also proposed in the previous studies.67, 68 𝑃𝑃𝐴𝑒𝑞 =

(𝐸𝑔𝑎𝑖𝑛,

𝐶𝐴

+ 𝐸𝑔𝑎𝑖𝑛,

𝑟𝑎𝑤

+ 𝐸𝑔𝑎𝑖𝑛,

𝐵𝐸)

𝐷𝐶𝐴 × 𝑃𝐶𝐴

× 𝐹𝑤

=

𝐸𝑔𝑎𝑖𝑛,

𝑡𝑜𝑡𝑎𝑙

× 𝐹𝑤

𝐷𝐶𝐴 × 𝑃𝐶𝐴

(8)

CONCLUSION This paper proposed a new technology for wastewater treatment of coking wastewater, A2BA1-system, enabling the extract of organic pollutants by activated carbon as simultaneous absorber and source of energy. The maximal volume loads of COD and NH3-N were 2.96 kg/(m3·d) and 0.15 kg/(m3·d) in the proposed system, respectively. Moreover, it was developed a novel methodology, EMMS, for assessment of energy saving in treating coking wastewater. This methodology integrated energy modeling, based on analysis of the in- and outgoing streams of the studied system, with the mathematical simulation analysis. The optimal operating conditions were obtained by the developed methodology and their average value of energy saving reached 48.3%. Low-cost and high-performance adsorbent enabled net energy gain for wastewater treatment. Consequently, the developed A2BA1-system was a promising technology and the EMMS was an effective methodology for coking wastewater treatment in terms of energy saving, promoting the sustainable development of wastewater treatment.

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ASSOCIATED CONTENT Supporting Information.

Detailed introduction of the A2BA1-system, details of energy

modeling, values of the basic variables, energy cost and gain in various operating conditions, objective function and constraints, and the LINGO 11.0. report.

ACKNOWLEDGMENTS We gratefully acknowledge the financial support from the Research and Development Foundation

of

Applied

Science

and

Technology

of

Guangdong

Province,

China

(No.2015B020235005), National Natural Science Foundation of China (No. 51278199 and No. B070302). The authors are grateful to Peter Jones for his help in editing this paper.

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(54) Zhao, Q.; Han, H.; Xu, C.; Zhuang, H.; Fang, F.; Zhang, L. Effect of powdered activated carbon technology on short-cut nitrogen removal for coal gasification wastewater. Bioresource Technol. 2013, 142(4), 179-185, DOI 10.1016/j.biortech.2013.04.051. (55) Yu, X.; Wei, C.; Wu, H.; Jiang, Z.; Xu, R. Improvement of biodegradability for coking wastewater by selective adsorption of hydrophobic organic pollutants. Sep. Purif. Technol. 2015, 151, 23-30, DOI 10.1016/j.seppur.2015.07.007. (56) Ren, J.; An, D.; Liang, H.; Dong, L.; Gao, Z.; Geng, Y.; Zhu, Q.; Song, S.; Zhao, W. Life cycle energy and CO2 emission optimization for biofuel supply chain planning under uncertainties. Energy. 2016, 103, 151-166, DOI 10.1016/j.energy.2016.02.151. (57) Ministry of Environmental Protection of the People's Republic of China. Emission standard of pollutants

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http://kjs.mep.gov.cn/hjbhbz/bzwb/shjbh/swrwpfbz/201207/t20120731_234146.shtml. (accessed on July 18th, 2017). (58) Rengaraj, S.; Moon, S.H.; Sivabalan, R.; Arabindoo, B.; Murugesan, V. Agricultural solid waste for the removal of organics: adsorption of phenol from water and wastewater by palm seed coat activated carbon. Waste Manage. 2002, 22(5), 543-548, DOI 10.1016/S0956-053X(01)00016-2. (59) Müller, F.; Dormann, H.; Pfistermeister, B.; Sonst, A.; Patapovas, A.; Vogler, R.; Hartmann, N.; Plank-Kiegele, B.; Kirchner, M.; Bürkle, T. Application of the Pareto principle to identify and address drug-therapy

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adsorption onto powdered activated carbon for micropollutant removal in advanced wastewater treatment. Water Res. 2014, 55(2), 185-193, DOI 10.1016/j.watres.2014.02.025. (61) Ou, H.S.; Wei, C.H.; Mo, C.H.; Wu, H.Z.; Ren, Y.; Feng, C.H. Novel insights into anoxic/aerobic(1)/aerobic(2) biological fluidized-bed system for coke wastewater treatment by fluorescence excitation-emission matrix spectra coupled with parallel factor analysis. Chemosphere. 2014, 113, 158-164, DOI 10.1016/j.chemosphere.2014.04.102. (62) Chinese Coking Industry Association (CCIA). Symposia of coking and environmental protection, China, 2015. http://www.cnljxh.com/index.html. (accessed on July 16th, 2017). (63) Cai, W.; Liu, W.; Yang, C.; Wang, L.; Liang, B.; Thangavel, S.; Guo, Z.; Wang, A. Biocathodic methanogenic community in an integrated anaerobic digestion and microbial electrolysis system for enhancement of methane production from waste sludge. ACS Sustain. Chem. Eng. 2016, 4(9), 4913-4921, DOI 10.1021/acssuschemeng.6b01221. (64) Hall, P.J.; Mirzaeian, M.; Fletcher, S.I.; Sillars, F.B.; Rennie, A.J.R.; Shittabey, G.O.; Wilson, G.; Cruden, A.; Carter, R. Energy storage in electrochemical capacitors: designing functional materials to improve performance. Energ. Environ. Sci. 2010, 3(9), 1238-1251, DOI 10.1039/C0EE00004C. (65) Zhi, M.; Yang, F.; Meng, F.; Li, M.; Manivannan, A.; Wu, N. Effects of pore structure on performance of an activated-carbon supercapacitor electrode recycled from scrap waste tires. ACS Sustain. Chem. Eng. 2014, 2(7), 1592-1598, DOI 10.1021/sc500336h. (66) Chen, D.; Li, Y.; Deng, M.; Wang, J.; Chen, M.; Yan, B.; Yuan, Q. Effect of torrefaction pretreatment and catalytic pyrolysis on the pyrolysis poly-generation of pine wood. Bioresource Technol. 2016, 214, 615-622, DOI 10.1016/j.biortech.2016.04.058. 32

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(67) Wu, Z.; Kong, L.; Hu, H.; Tian, S.; Xiong, Y. Adsorption performance of hollow spherical sludge carbon prepared from sewage sludge and polystyrene foam wastes. ACS Sustain. Chem. Eng. 2015, 3(3), 552-558, DOI 10.1021/sc500840b. (68) Lee, D.W.; Jin, M.H.; Oh, D.; Lee, S.W.; Park, J.S. Straightforward synthesis of hierarchically porous nitrogen-doped carbon via pyrolysis of chitosan/urea/KOH mixtures and its application as a support for formic acid dehydrogenation catalysts. ACS Sustain. Chem. Eng. 2017, 5(11), 9935-9944, DOI 10.1021/acssuschemeng.7b01888.

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List of Figures. Fig. 1. Process diagram of the A2BA1-system for coking wastewater treatment. Fig. 2. Variation in operational performance of the biological treatment system. (a) COD, (b) NH3-N, (c) NO3-N and TN. Fig. 3. Classification and determination of basic variables. Fig. 4. Energy saving distribution of the A2BA1-system with different COD concentrations of raw wastewater and treated water. Fig. 5. Energy saving distribution of the A2BA1-system with different AC dosages. Fig. 6. Energy saving values of the A2BA1-system with different AC additions. (a) 1.00 g/L, (b) 2.00 g/L, (c) 3.00 g/L, (d) 4.00 g/L. Fig. 7. Energy saving rate for different operating conditions.

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Fig. 1. Process diagram of A2BA1-system for coking wastewater treatment.

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(a) COD

(b) NH3-N (b)

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(c) NO3-N and TN

Fig. 2. Variation in operational performance of the biological treatment system.

Determined in experiments CCOD1, raw

CCOD2, raw

Encombustion, raw

Di

Encombustion, CA

DCA

CODr, bioreactor TOCr, bioreactor BOD5,r

Encombustion, BE

CCOD1, BE

CCOD2, BE

Design handbook

Market survey

Fw, Pfan, Qfan, RO2, ρO2, ρO2, φO2, α, β, γ, ε, δ, σ

PAC , Pep , Pi , PCO2 , Psludge

Fig.3. Classification and determination of basic variables. 37

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Fig. 4. Energy saving distribution of the A2BA1-system with different COD concentrations of raw wastewater and treated water.

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Fig. 5. Energy saving distribution of the A2BA1-system with different AC dosages.

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(a)

(b)

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(c)

(d)

Fig. 6. The energy saving values of the A2BA1-system with different AC additions. 41

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(a) 1.00 g/L, (b) 2.00 g/L, (c) 3.00 g/L, (d) 4.00 g/L.

Fig. 7. Energy saving rate for different operating conditions.

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List of Tables: Table 1. Design parameters of the combined bioreactor (O1-H-O2). Table 2. Primary characteristics of the raw coking wastewater collected from Shaoguan Steel Company. Table 3. Basic variables and their value ranges used in energy modeling. Table 4. Detailed procedure of the ABC method.

Table 1. Design parameters of the combined bioreactor (O1-H-O2). Ve (L)

pH

T (ºC)

SRT(day)

HRT (h)

DO (mg/L)

O1

10.00

7.5±0.5

28.0±3.0

30

8.0

2.0-4.0

H

15.00

8.0±0.5

28.0±3.0

90

12.0

0.2-0.4

O2

18.75

7.5±0.5

28.0±3.0

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15.0

2.5-4.5

Table 2. Parameters of the raw coking wastewater collected from Shaoguan Steel Company a. pH

COD (mg/L)

BOD5 (mg/L)

NH3-N (mg/L)

SCN(mg/L)

CN(mg/L)

S2(mg/L)

Oil (mg/L)

VP b (mg/L)

9.9 ± 0.6

4838.7 ± 109.7

1376.6 ± 35.1

195.3 ± 17.9

276.8 ± 25.2

37.9 ± 4.3

134.2 ± 9.9

157.4 ± 15.8

1099.1 ± 28.9

a Average b VP-

value ± standard deviation

volatile phenolic compounds

Table 3. Basic variables and their value ranges used in energy modeling.

𝑉1

B1

B2



Bi



Bn

Scope

C11

C12



C1i



C1n

[L1, U1]

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𝑉2 ... 𝑉𝑘 𝐸𝑐𝑜𝑠𝑡 𝐸𝑔𝑎𝑖𝑛 𝐸𝑛𝑒𝑡

C21 … Ck1 𝐸𝑐𝑜𝑠𝑡,1 𝐸𝑔𝑎𝑖𝑛,1 𝐸𝑛𝑒𝑡,1

C22 … Ck2 𝐸𝑐𝑜𝑠𝑡,2 𝐸𝑔𝑎𝑖𝑛,2 𝐸𝑛𝑒𝑡,2

… … … … … …

C2i … Cki 𝐸𝑐𝑜𝑠𝑡,𝑖 𝐸𝑔𝑎𝑖𝑛,𝑖 𝐸𝑛𝑒𝑡,𝑖

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… … … … … …

C2n … Ckn 𝐸𝑐𝑜𝑠𝑡,𝑛 𝐸𝑔𝑎𝑖𝑛,𝑛 𝐸𝑛𝑒𝑡,𝑛

[L2, U2] … [Lk, Uk]

Where, Bi represents the i-th combination of operating conditions and the corresponding concentration or dosage are C1i, C2i, … , Cki, respectively.𝐸𝑐𝑜𝑠𝑡,𝑖 stands for energy cost of the i-th combination (kWh/m3). 𝐸𝑔𝑎𝑖𝑛,𝑖 stands for energy gain of the i-th combination (kWh/m3). 𝐸𝑛𝑒𝑡,𝑖 stands for net energy gain of the i-th combination (kWh/m3). Lk and Uk are the lower and upper limit of the k-th variable, respectively.

Table 4. Detailed procedure of the ABC method. First

Second

Third

Forth

Fifth

Original data (xi)

Single ratio (Ri)

Rearrange Ri from large to small (RRi)

Cumulative frequency (CFi)

Determination of the corresponding operating conditions of class-A

x1

R1= x1/Sum

x2

R2=x2/Sum





xk

Rk=xk/Sum





RR1= LARGE (R1:Ri, 1)a RR2= LARGE (R1:Ri, 2) … RRk= LARGE (R1:Ri, k) …

xi

Ri= xi/Sum

RRi= LARGE (R1:Ri, i)

Sum=

CF2= RR1+ RR2 … 𝑘

CFk=∑𝑖 = 1𝑅𝑅𝑖 … 𝑖

CFi=∑𝑖 = 1𝑅𝑅𝑖 Determination of threshold value (TV) of class-A

𝑖

∑𝑖 = 1𝑥𝑖 a

CF1= RR1

LARGE is a function existed in the Excel to arrange the numbers from large to small.

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The upper of TV of class-A

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Graphical Abstract

A methodology for energy saving assessment (named EMMS) is developed based on the practical material flows in the wastewater treatment. In return, the optimal operating conditions can be achieved using EMMS, which are helpful for the sustainable development of wastewater treatment in terms of

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energy saving.

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