A Quantitative Assessment Model for the Environmental Issue inGold Production†

Caiwu Lu1, Jianxian Wei2, Fumin Wang3 and Xinzhe Lan4

ABSTRACTThis paper presents a quantitative assessment model for the assessmentof environmental load in gold production, which is based onenvironmental ecology, Life Cycle Assessment (LCA) and artificialneural network. In LCA evaluation indexes include resourceconsumption, energy consumption, gas waste, liquid waste, solid wasteand so on. Each item represents an aspect in gold production while theircombination reflects the relationship between gold production andenvironment. In this paper an utility function of evolution index onenvironmental load is given by linear or non-linear transformation, whichchanges evaluation indexes with different dimension into an equivalentvalue in range of [-1, 1]. This paper also provides a new approach tomake decision on the weight for quantitative assessment with the help ofa three-layered artificial neural network. The proposed approach in thispaper has been successfully used to a real synthetic assessment for theenvironmental load in gold production, which include seven kinds ofdifferent operational technologies in China. The applications prove thatthe new approach is successful and reliable. It provides a new tool for theassessment of environmental load of the gold production.

INTRODUCTION

A great deal of mineral resources is being mining in order todevelop the material civilisation, where a lot of by-product(waste residue, waste gas and waste water, etc) is emerged andabandoned resources are disappeared. Since mineral resource islimited and can be exhausted, how to make full use of mineralresource and reduce the environment maladjustment havebecome severe problem to the global (Xi Deli, 1995). Obviously,it is necessary to study the impact of mineral resource productionon environment.

There are many different operational technologies in goldproduction, such as Thiourea method, Thiosulfate method,Thiosulfite method, LSSS method, Modified LSSS method,Polysulfide method and Cyanide method. Each gold productionmethod can consume different resource and energy; it alsoproduces different exhaust gas and wastewater, etc. In order toassess the environmental load of each gold production method,this paper presents a quantitative assessment model, which isbased on environmental ecology, Life Cycle Assessment (LCA)and technical conditions of gold production in China.

LIFE CYCLE ASSESSMENT

Life Cycle Assessment (LCA) is the primary theory andanalysing method for industrial ecology (ISO/DIS 14040(Committee Draft), 1995). It is a process to evaluate theenvironmental load associated with a product, process, or

activity. The evaluation is based on the identification andquantitative computation on energy and material consumptionsand wastes released to the environment in order to assess theirimpact on environment. The assessment includes the entire lifecycle of the product, process, or activity, including extractingand processing raw materials; manufacturing, transportation anddistribution; use and re-use, maintenance; recycling and finaldisposal.

Because Life Cycle Assessment is still at research phase, it hasnot been used at large scale and it must be improved in thecomparability and reliability of assessment. The research of LCAmainly emphasises on its principle and concept, it lacks concretepractice of assessment.

Life Cycle Assessment is composed of four parts (see Figure1): goal definition and scope, life cycle inventory analysis,impact assessment and improvement assessment.

Goal and scope

The object of assessment in LCA is a system or process. Thegoal of assessment is to make sure the impact of the system orprocess on environment, which can finally be evaluated as a data.Therefore, the first step is to define the goal and scope inassessment. It is also the precondition to go to the next phase:inventory analysis.

Inventory analysis

Inventory analysis is a quantitative description to all substanceand energy that input or output to/from the system or process. Itis the beginning of quantitative assessment in LCA and thefoundation to obtain a unique index to evaluate environmental

Mine Planning and Equipment Selection Kalgoorlie, WA, 23 - 25 April 2003 465

1. Xian University of Architecture and Technology, Xi’an, ShaanxiProvince 710055, PR China. E-mail: caiwulu@pub.xaonline.com

2. Senior Engineer, Bureau of Hanxing Metallurgy Mine, Handan,Hebei Province, 056032, PR China.

3. Senior Engineer, Bureau of Henan Gold, Zhengzhou, HenanProvince, 450003, PR China.

4. Xian University of Architecture and Technology, Xi’an, ShaanxiProvince 710055, PR China.

† This project is supported by Huo Yingdong Education Foundation ofChina.

goal definitionand scope

Using, reusing and maintaining

Callback

Assigning, transportation

Making, machining and moulding

Treat with castoff

Raw material mine

Impact assessment Improvement assessment

Inventory analysis

Raw material

Energy

Input Output

Liquid waste

Solid waste

Gas waste

Other castoffs

Byproducts

FIG 1 - The technique framework of LCA.

load finally. The items of inventory analysis usually includeresource consumption, energy consumption, gas waste, liquidwaste and solid waste.

Impact assessment

Depending on the inventory analysis, impact assessment is to getan index, which can measure the environment load of the systemor process. It is a connection between inventory analysis andenvironmental load; it can outline the contour of the influence bythe system to be assessed on environment. The process of impactassessment can be divided into three steps: classification,characterisation and valuation.1. Classification is a process of permutation and combination

according to the type of environmental load and inventoryitems; it can usually be divided into resource consumption,load to human health, load to environment, etc.

2. Characterisation is to summarise the inventory itemsconnecting to environmental load; it can be calculated asvalue for each environmental load.

3. Valuation is to get a value for the final assessment ofenvironmental load by summing up each environmentalload with different weight.

Improvement assessment

Improvement assessment is to get an assessment conclusion andpropose some suggestion to reduce the environmental load andimprove the environment.

By all appearances, LCA is a pre-digested model; its principlecan be employed in all production departments.

INDEXES FOR THE UTILITY ASSESSMENT ANDITS CALCULATION

The assessment indexes of LCA include many items such asresource consumption, energy consumption, gas waste, liquidwaste, solid waste and so on. Each item represents an aspect ingold production while their combination reflects the relationshipbetween gold production and environment. The characteristics ofthese indexes are as follows:1. These indexes have different unit such as ton or cubic

meter, they cannot be compared with each other directly.2. The features of these indexes are different from each other.

For example, there are different physics meaning forresource consumption, energy consumption, gas waste,liquid waste and solid waste even they have the same unit,they cannot be compared with directly too.

3. The numerical values of these indexes are quite differenttoo. Due to the difference in assessment standard andnumerical criterion, the numerical value of one index maybe larger than another, but its value on environmental loadmight be smaller than the latter. For example, the numericalvalue of resource consumption is much larger than thenumerical value of harmful gas, but its environmental loadvalue is smaller than the latter.

We define an index for the utility assessment on environmentalload for an index as follows:

P fCS

j nij jj

j

=

=1 2, ,...,

where:Pij the utility assessment on environmental load for factor j in

assessment index i , there are n factors in index I

Cj the environmental load value of factor j for assessmentindex I

Sj the allowable value or standard of environmental load offactor j for assessment index i, it represents the basisrequirement by human being to environment

fi the utility function (load value) of factor j, its value is in theregion of [0, 1]

It is important to construct the utility function fi since it is atransformation function. Usually, the value of utility function isselected from region [0 1] (Hamilton, 1995), this value cannotrealise the principle of ‘reward good and penalise bad’. In otherwords, when the environmental load value is under the nationalstandard, the utility function should be negative so that rewardcan be realised. On the other hand, when the environmental loadvalue exceed the nation standard, the utility function should bepositive. Hence, we can define the utility function of assessmentindex on environmental load as follows:

Assume x C SS

i i

i

= −

where:x the relative value between the environmental load and the

national standard of the assessment index iSimilar to the learning function in artificial neural network,utility function can be defined as:

f x ee

kx

kx( ) = −+

−

−

11

Function f(x) is a curve with type S, the value of K mainlydepends on the influence of environmental index. Take noxiousgas as an example, when it changes a little, it will be harmful toour health. Therefore, K should be either a very big value or avery small value.

In general, utility function can be represented as:

P eei

kx

kx= −+

−

−

11

where:

x C SS

i i

i

= −

QUANTITATIVE ASSESSMENT FOR THEENVIRONMENTAL LOAD

The main environmental loads of gold production are resourceconsumption, energy consumption, gas waste, liquid waste andsolid waste according to the LCA model (Liu Jianglong, Li Huiand Ding Peidao, 1996; Liu Jianglong, Qian Xiaorong and DingPeidao et al, 1998). After the single index of utility assessmentof the gold production has been defined, we can construct aquantitative assessment model for the environmental load of goldproduction as follows:

EF P

ELV EF

i ijj

k

ii

=

=

=

=

∑

∑1

1

4

ωi

where:EFi an equivalent value of the environmental load

corresponding to index of resource, energy, gas waste andliquid waste respectively (solid waste may not be comparedin gold production)

466 Kalgoorlie, WA, 23 - 25 April 2003 Mine Planning and Equipment Selection

CAIWU LU, JIANXIAN WEI, FUMIN WANG and XINZHE LAN

ω i the weight of index i. Obviously, ωii

k

=∑ =

1

1

ELV environmental load valueThe assessment of environmental load is based on the

weighted sum of different equivalent value of the environmentalload. Obviously, it is important for the assessment to determinethe weight of each index. This paper provides a new approach tomake decision on the weight for quantitative assessment with thehelp of artificial neural network (Salt et al, 1992).

The assessment model is a three-layered model in artificialneural network as in Figure 2. The first layer is input-layer withfour nodes (Z1, Z2, Z3, Z4 represent resource consumption,energy consumption, liquid waste and gas waste respectively),one can input equivalent value of the environmental load for eachnode. The second layer is a hide-layer with seven nodes (Y1, Y2,Y3, Y4, Y5, Y6, Y7), which is used to adjust the relation betweendifferent indexes. The third-layer is an output-layer with uniquenode (H), which represents the final value of environmental loadfor gold production. Vij is the weight connecting input-layer andhide-layer; ω i is the weight connecting hide-layer and output-layer. Here, the learning algorithm for neural network is abackward-propagation algorithm (BP algorithm).

The learning algorithm is as follows:1. Initialisation. Let learning rate η = 0.01, momentum

coefficient α = 0.1, Zi = Yi = -1.2. Select Vij and ω i as random data between -0.5 and 0.5.3. Calculate output of each node from input-layer to

output-layer:

net V Z i

Y f net

net Y

i ij jj

i i

i ii

= =

=

=

=

=

∑

∑

1

5

1

7

1 2 7, , , ...,

( )

ω

H f net

f x ee

x

x

=

= −+

−

−

( )

( ) 11

4. Calculate the system error. If there are n training samples,the average variance for system error is:

En

D Hi ii

n

= −=∑1

22

1

( )

where:Di the expecting output of training sample iHi the actual output of training sample i

5. If E is less than required error or iterative steps are greaterthan required one, the learning algorithm terminates.

6. Calculate the learning signal δ of each node:δδ ω δ

0

0 1 2 7= − ′= ′ =

( ) ( )( ), , , ...,

D H f netf net ii i i

7. Modify the weight along the descend direction of gradient:

( ) ( )ω ω ηδ α[ω ω

ηδi i i i

ij ij

t t Y t t

V t V t

( ) ( ) ]

( ) ( )

+ = + + − −

+ = +

1 1

10 i

( ) ( )i i ij ijZ V t V t+ − −α[ ]1

8. Go to step (3).

CALCULATION EXAMPLES IN GOLDPRODUCTION

There are seven kinds of different operational technologies ingold production, such as Thiourea method, Thiosulfate method,Thiosulfite method, LSSS method, Modified LSSS method,Polysulfide method and Cyanide method. They can be sorted insequence according to their environmental load.

Establishment of training samples

The training samples are shown in Table 1, their values ofenvironmental load are in region of [-1, 1]. Training samples 1, 2and 3 are in ideal status, training sample 4 and 5 correspond togold production operational technology 1 (Thiourea method) and7 (Cyanide method) respectively, their load value are confirmedby the local authorities.

We use five samples of Table 1 as training sample to establish

a BP model of artificial neural network, which has fourinput-nodes, 7 hide-nodes and one output-node. The trainingsamples are input into the model to train the network accordingto the steps 1 ~ 8 as described in previous section. Aftercalculation of 73 iterations, its average variance of system errorE<0.0001, and the artificial neural network can completelyidentify all samples (see Table 2). Its identification rate isapproximate to 100 per cent.

Mine Planning and Equipment Selection Kalgoorlie, WA, 23 - 25 April 2003 467

A QUANTITATIVE ASSESSMENT MODEL FOR THE ENVIRONMENTAL ISSUE IN GOLD PRODUCTION

FIG 2 - Three-layered BP model in artificial neural network.Sample Load type Load

valueResource Energy Liquid waste Gas waste1 -1 -1 -1 -1 -12 0 0 0 0 03 1 1 1 1 14 0.0528 -0.2193 -0.3214 -0.4853 -0.30255 -0.2080 0.6591 0.8011 0.9911 0.5608

TABLE 1Training samples of environmental load.

Sample number 1 2 3 4 5Trained value -1.0002 -0.0063 0.9987 -0.2980 0.5611Expecting value -1 0 1 -0.3025 0.56Relative error (%) 0.02 0.63 0.13 1.4876 0.1964

TABLE 2The learning result of artificial neural network.

Then, the complex relationship between the environmentalload type and environmental load value of gold production areset up. The result would be more accurate if the number ofsamples were large enough.

The calculation of single index for environmentalload of gold production

Each gold production method must consume a lot of resourceand energy, at the same time it also produces a great deal ofexhaust gas and waste water; for example, in order to deal with1000 kilograms of ore which contain five grams of gold, theThiourea method can be used. It will consume 7.6 mol thiourea,12.6 mol sodium sulfite, 20 mol ferrous sulfate, 9.8 mol sulfuricacid and about 2.286 × 107 joule energy (including heat energyand mix round energy), produce waste water and waste gaswhich have 70 mg/L thiourea, 1800 mg/L sulfate, about 0.32mol/L sulfur dioxide. Then the single index of Thiourea methodfor the utility assessment on environmental load can becalculated according to the formula in this paper discussedearlier under the heading ‘indexes for the utility assessment andits calculations’, the result as follow:Resource load is: 0.0528Energy load is: -0.2193Liquid waste load is: -0.3214Gas waste is: -0.4853

Figure 3 illustrates the calculation result for the equivalentvalue of the environmental load of single assessment index,which is based on seven kinds of different operationaltechnologies in gold production.

Table 3 illustrates the calculation result for the equivalentvalue of the environmental load of single assessment index.

The environmental load assessment of goldproduction

Using the three-layered artificial neural network trained, theenvironmental load value can be calculated by inputting the valueof single index for the environmental load of each operationaltechnology for gold production (see Table 3), the result is shownin Table 4.

468 Kalgoorlie, WA, 23 - 25 April 2003 Mine Planning and Equipment Selection

CAIWU LU, JIANXIAN WEI, FUMIN WANG and XINZHE LAN

Number Alternative name (method) Load typeResource Energy Liquid waste Gas waste

1 Thiourea 0.0528 -0.2193 -0.3214 -0.48532 Thiosulfate 0.1030 -0.0848 -0.2335 -0.46663 Thiosulfite 0.0905 -0.0810 -0.2335 -0.46664 LSSS 0.0358 -0.1288 -0.2272 -0.46665 Modified LSSS -0.0804 -0.1792 -0.2940 -0.48186 Polysulfide 0.0092 -0.0912 0.2368 -0.49647 Cyanide -0.2083 0.6591 0.8011 0.9961

TABLE 3The result of single assessment index on environmental load.

(1) Thiourea method, (2) Thiosulfate method, (3) Thiosulfitemethod, (4) LSSS method, (5) Modified LSSS method,

(6) Polysulfide method and (7) Cyanide method

FIG 3 - The environmental load of single index for seven kinds ofdifferent operational technologies.

Number Alternative name (method) Load type Load valueResource Energy Liquid waste Gas waste

1 Thiourea 0.0528 -0.2193 -0.3214 -0.4853 -0.2982 Thiosulfate 0.1030 -0.0848 -0.2335 -0.4666 -0.23233 Thiosulfite 0.0905 -0.0810 -0.2335 -0.4666 -0.23724 LSSS 0.0358 -0.1288 -0.2272 -0.4666 -0.25745 Modified LSSS -0.0804 -0.1792 -0.2940 -0.4818 -0.38296 Polysulfide 0.0092 -0.0912 0.2368 -0.4964 -0.11297 Cyanide -0.2083 0.6591 0.8011 0.9961 0.5611

TABLE 4The result of environmental load forecast by artificial neural network.

The integrative environmental load value can be sorted insequence as: Cyanide method, Polysulfide method, Thiosulfatemethod, Thiosulfite method, LSSS method, Thiourea method andModified LSSS method according to the Table 4. Theenvironmental load value of Cyanide method is the biggest oneand the Modified LSSS method is the smallest, which are incoincidence with the fact. It proves that the new assessmentapproach is successful and reliable.

CONCLUSION

1. A utility function of assessment index on environmentalload is given in this paper by linear or non-lineartransformation, which is based on the concept ofenvironmental index in ecology and utility in economics.

2. A method that can changes assessment indexes withdifferent dimension and different meaning into anequivalent value in range of [-1, 1] is put forward. Bymeans of utility function it can emphasises not only on theprinciple of ‘reward good and penalise bad’ but also makesit easy to train artificial neural networks.

3. A quantitative assessment model for the assessment ofenvironmental load in gold production is proposed in thispaper. This model can make environmental assessment interms of a set of indexes and quantitative approach for goldproduction and research techniques. It has many advantagessuch as calculating briefness, evaluating impersonality,strong sufferance of error and high reliability.

4. The proposed approach in this paper has been successfullyused to a real synthetic assessment for the environmentalload in gold production, which includes seven kinds ofdifferent operational technologies (Thiourea method,Thiosulfate method, Thiosulfite method, LSSS method,Modified LSSS method, Polysulfide method and Cyanidemethod) in China. The environmental load value ofCyanide method is the biggest one and the Modified LSSSmethod is the smallest, which are in coincidence with thefact. It proves that the new assessment approach issuccessful and reliable.

5. This paper provides a new tool for the assessment ofenvironmental load of the gold production.

REFERENCESISO/DIS 14040 (Committee Draft), 1995. Environmental Management –

Life Cycle Assessment Principles and Framework.Hamilton, K, 1995. Sustainable Development, the Hartwick Rule and

Optimal Growth, Environmental and Resource Economics, pp393-411.

Liu Jianglong, Li Hui and Ding Peidao, 1996. The quantitativeassessment model for the environmental load of material, AcademicJournal of Lan Zhou University, 32:56-63 (in Chinese).

Liu Jianglong, Qian Xiaorong and Ding Peidao, et al, 1998. Universalenvironmental Loadings and an Environmentally EconomicalBenefit-cost Analysis for Iron and Steel Materials, Advances inEnvironmental Science, 6(4):64-69 (in Chinese).

Salt, D W, et al, 1992. The Use of Artificial Neural Networks in QSAR,Pestic Sci, 36(2):161.

Xi Deli, 1995. Cleanliness Production, pp 167-195, Chongqing UniversityPublishing Company (in Chinese).

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A QUANTITATIVE ASSESSMENT MODEL FOR THE ENVIRONMENTAL ISSUE IN GOLD PRODUCTION