Problem Chosen Mathematical Contest in Modeling (MCM) Summary Sheet (Attach a copy of this page to each copy of your solution paper.

Transcription

1 Team # 8 Page of 29 Team Control Number For offce use only T T2 T3 T4 8 Problem Chosen C For offce use only F F2 F3 F Mathematcal Contest n Modelng (MCM) Summary Sheet (Attach a copy of ths page to each copy of your soluton paper.) Type a summary of your results on ths page. Do not nclude the name of your school, advsor, or team members on ths page. Summary All of the nonrenewable resources n our world are beng depleted gradually, especally the ol. Ths paper presents our researches of the estmaton of ol consumpton, and the dea of evaluatng relevant polces. In general, we have developed three models wth two purposes. The frst two models are proposed for the predcton of ol consumpton. One s the applcaton of Logstc Model and t only takes advantage of hstorc data. The fully usage of accurate data has dsplayed ts exacttude and convenence. The second model s based on the lnear neural network theory. We reckon the whole complex relatonshp as a neuron, t nputs by some factors that are assocated wth ol consumpton (e.g. economc, demographc, poltcal, envronmental factors). However, outputs the ol consumpton. In ths model development, we do not fall nto the stuaton of analyzng the complcated relatonshp between ol consumpton and correlatve factors, whch s hard to present. Instead, the complex stuaton s smulated by a neuron and we only can take care of the way of nputs and output. So ths approach can ensure the model open-ended. And we can apply t on predctng other nonrenewable resources. The last model s actually a multple objectve programmng. Two objectves, one s for cut the ol consumpton maxmumly and the other s for decreasng the cost of polces. Through the last model, we completed the quanttatve evaluaton of polcy eventually. Moreover, t s appled for judgng our ol-related polces.

2 Team # 8 Page 2 of 29 Quanttatve Evaluaton of Ol Polces 电子科技大学 王超 陈智 伍剑锋 Introducton Nobody would deny that the world runs on ol. The world consumpton of ol has ncreased all along snce 985. In 2002, the global demand of ol reached at 78 mllon barrels per day. [Energy Informaton Admnstraton. 2004] As the ol s a knd of nonrenewable resource, we have to pay enough attenton to ts explotaton and the way people use. As well as the ol consumpton contnues ncreasng, n order to reserve enough resource for the future energy crss, t s very necessary to be accurate for the estmaton of ol depleton. In ths paper, three models are proposed. The frst uses the Logstc model to estmate the cumulatve ol consumpton over a long horzon. The second, based on the lnear neural network theory, s used to predct future ol consumpton. The last model s a multple objectve programmng, whch can provde quanttatve evaluaton of polces. We propose the three models n a logcal order. The frst provde estmaton of ol consumpton so that we can use t to be output data n the desgnng the neural network, whch s the second model. And the network provdes predcton of the effect of the polces whch are evaluated by the thrd one. Analyss of the Problem Facng the contnual depleton of all sorts of nonrenewable and exhaustble resources, we decde to propose some models to forecast ts consumpton and evaluate polces n order to sustan ther usage through a long perod of tme. Meanwhle, We hope the models we proposed have provded an extensve (feld) applcaton. So the open-ended consderaton s fully taken nto account n our whole model development. Then we decde to choose ol as our analyss example. Because t s the most appled energy and t has a huge mpact on development of human socety. To acheve our am, we plan to construct three models one by one. Frstly, we should develop a model to fnd out the regulaton of annual ol consumpton. We ntend to use curve-fttng method to formulate a functon of tme n order to pont t out. Consderng the lmted reserves, the commonly used Logstc model mght be a good method to smulate the change of the cumulatve ol consumpton. And by calculatng the dervatve of the Logstc functon we would get the annual ol consumpton. Secondly, we take economc, demographc, poltcal and envronmental factors nto account to develop a more adaptable model, whch can forecast consumpton of ol n the future, so that we can have an estmaton of the effect of our polces. We hope ths model can be appled not only to ol projecton but also to the projecton of other nonrenewable mneral and resources. Thus, a flexble neural network can be appled n our model.

3 Team # 8 Page 3 of 29 Fnally, we gve polces on harvestng, securty, control of envronmental effects and scentfc research to releve the pressure of energy crss. We would lke to propose a model to evaluate polces. From the vewpont of decson maker, we hope the effect of polces on reducng ol consumpton s greatly successful, whle the cost of mplementaton of these polces s small. So we ntend to formulate a model wth two objectve programmng. Then we could evaluate the polces more reasonably as well as makng more scentfc decsons. Model Development Estmated Ol consumpton Wth ol nonrenewable, t started dryng up when we consumed the frst barrel. However, when the requrement of ol s gettng smaller, the mmense reserves of ol does not content human consumpton. On the other hand, wth the ncreasng of populaton, consumpton of ol has become much more. the growth rate of ol would declne gradually untl the lmtaton of ol gross beng effectve,. Thus, as human socety contnues developng, the growth rate of ol would slow down from an ntal hgh speed, but the accumulatve consumpton wll stll grows. Through our rough analyss, we can draw the followng fgure qualtatvely. Fgure. Trend of Accumulatve Ol Consumpton From the above analyss and the fgure, we can ndcate the growth rate of ol consumpton s jontly proportonal to the current consumpton and the avalablty for new requrement growth. The accumulatve consumpton approaches an upper bound along wth the tme toward and a lower bound along wth the tme backward. That s qute smlar to populaton growth. So we decde to use a populaton model to smulate the accumulatve consumpton of ol. Introduced a classc populaton model Logstc Model [Texas Instruments Incorporated, 2000] N( t) = K + ( N 0 K ) e mt, t 0 Where t s the year, N(t) s the populaton and m s the net growth rate of populaton. Then we redefne that N(t) s the accumulatve consumpton of ol; m s the net growth

4 Team # 8 Page 4 of 29 rate of ol consumpton. We use the data of total consumpton of ol n Table X, wth the curve fttng tool n MATLAB. We can get m = , at the same tme goodness R 2 = We substtute m = nto N(t), then dfferentatng and smplfyng yelds e Annual Ol Consumpton = 54 ( e The estmated annual ol consumpton s drawn n the followng fgure x x ) 2 Fgure 2. Estmated Annual Ol Consumpton The result ndcates that f there s not adequate polcy carred out for the lmtaton of ol consumpton, t wll be depleted n a few years. Hence we should take varous polces to avod rapd exhauston of the resource or severe dsrupton of consumpton; the polces should nclude economc polcy, harvestng polcy, envronment polcy and so on. Lnear Neural Network for the Increasng Ol Consumpton The ol requrement s determned by varous factors ncludng much haphazard that we can hardly formulate a smple functon to llustrate the relatonshps between them. Thus, we plan to solve ths problem from a macroscopcal vewpont: a lnear approxmaton s desred. That s to say, the consumpton of resources can be denoted by a lnear combnaton of several factors. Nevertheless, ths functon s statc and lack of flexblty, unless we treat t as a neuron of Lnear Neural Network. A lnear neuron s descrbed lke ths [MATLAB Famly Product Help, 2002],

5 Team # 8 Page 5 of 29 Fgure 3. Archtecture of Sngle-Neuron Lnear Network In our network, whch s desgned for ol consumpton, there s only a sngle neuron. The factors are regarded as ts nputs, whle consumpton of ol s ts output, and the weghts between consumpton and factors are regarded as the value of the connecton. As Fgure 4 shows: Fgure 4. Structure of Our Lnear Neural Network The process of the development of a network s summarzed as follows: Step. we construct the archtecture of the network, t means, to ndcate roughly how the network transforms the nputs nto an output; Step 2. we provde the nput data and output data, so that network can learn to modfy the weghts and bas to ncrease the accuracy of the transformaton. Step 3. we provde more nput data (of course there s no output correspondng to them), then the network can use the experence t has obtaned before to transform the nputs nto output, whch s the predcton. Focusng on the ol consumpton problem, we begn to desgn our lnear neural network: Step. Above all, we should fnd out the factors that are closely relevant to the ol consumpton as our nputs. Everyone should admt that human s lfe depends on ol. There s no doubt that requrement of ol would ncrease as populaton grows. And obvously, ol consumpton has close relatonshp wth world economc growth.next, we thnk the prmary consequence on envronment of ol depleton, the greenhouse effect, s manly due to emsson of CO 2. So the CO 2 growth rate n the atmosphere can roughly ndcate the world ol consumpton. Addtonally, ol prce can determne ts consumpton drectly. If the prce reaches an unreasonable level, people would fnd alternatve energy spontaneously under the prce pressure. Further more, Energy

6 Team # 8 Page 6 of 29 structure wll change wth technology development and can remarkably affect the requrement of ol. Thus, we thnk the factors above can manfest the ol consumpton most sgnfcantly. So nputs of our Neural Network system are: World Annual Economc Growth World Annual Populaton World Annual CO 2 Growth World Annual Ol Prce World Annual Energy Structure Step 2. After choosng the nputs of neuron, the structure of the sngle-neuron lnear network has been constructed. Snce MATLAB can ntalze our network whle determng the weghts and bas smultaneously, the mmedate need s to provde a data sheet for t. From Table. we get world prmary energy consumpton of dfferent energy sources. Each year, we use consumpton of all the sources to dvde consumpton of ol, then we obtan the rato of ol n the world energy structure. From Fgure 2 n the Appendx, we evaluate the average growth rate of the world economy.and data sets of energy structure, CO 2, populaton and world economc growth rate are provded yearly, whle that of the ol prce s presented n a dfferent way, whch noted yearly from 990 to 996 and weekly snce 997. As nsuffcent amount of sample of data wll greatly affect the projecton capablty of our model, we make 0.-year as a step n the whole perod. We use MATLAB software to do the cubc splne nterpolaton, and fnally obtan a data matrx, whch contans the value of populaton, world economc growth rate, amount of CO 2 n the otmosphere, rato of ol n the energy structure, and ol prce, every 0.-year from 990 to 2004By our frst model, we can calculate consumpton of ol n any year, but the nput data are presented every 0. year. However, we could use N(t) to estmate the accumulatve consumpton of ol from 990 to 2004 by 0.-year step. And the consumpton of every 0. year can be obtaned easly. However, there s stll one more job to do. As the data are n dfferent dmenson,,not comparable, normalzaton sndspensable. We normalze data of nputs and output, so that they arrve at the nterval [-,], by the algorthm as follows: 2 ( q mn{ }) * q q = max{ q } mn{ q } q where just represents a value n a certan data vector, and represents normalzed value. After that, we use the data of the former ten years to desgn the network and smulate the network behavor to ensure the desgn was done properly. Step 3. To test the predcton ablty of our Neural Network, we smulate the network behavor wth the later 5-year data sets. The results are shown as follows, compared wth the consumpton of ol evaluated by our frst model: [see program n the reference] * q

7 Team # 8 Page 7 of 29 Table. Result of Smulaton (wth 0 Years Learnng) (Bllon barrels per year) Model Network Model Network Model Network Fgure 5. Result of Smulaton (wth 0 Years Learnng) The sold-style curve shows the consumpton of ol estmated by our frst model, and the dot-style curve shows the target value estmated by our lnear neural network. Although the value of ol consumpton wth later 5-year data sets predcted does not perfectly match the practcal value, we are stll content wth t. Because our fundamental data, from whch the Neural Network learns, s only ten years. How s an nfant able to walk steadly before he learns to? The more you teach hm, the better he performs. If we let hm learn from the former 4 years data, and then test hs predcton ablty n the 5 th year, we can get a surprsng result, as shown below: Table 2. Result of Smulaton (wth 4 Years Learnng) (Bllon barrels per year) Model Network Model Network Model Network

8 Team # 8 Page 8 of 29 Fgure 6. Result of Smulaton (wth 4 Years Learnng) The sold-style curve shows the consumpton of ol estmated by our frst model, and the dot-style curve shows the target value estmated by our lnear neural network. From the fgure we fnd that the two curves are too close to dstnct. It means network outputs are qute close to the desred targets. Nevertheless, we do not halt here. We can t wat to see the consumpton n future predcted by our network, although we can foresee the result maybe not satfyng for shortage of data sets. Above all, we should provde more data as nput to our network. However, to collect data of all the factors n the future s a hard work. It s necessary to make some smplfcaton. Snce Fgure 6 above has demonstrated the predcton ablty of our network, we beleve that the weghts t determnes s relable. After checkng the weghts that the network determnes, we get Table 3. Weghts Determned by Network World Rato n Factors Populaton CO2 Economc Ol Prce Energy Growth Structure Weghts Thus, we can fgure out that world populaton and world economc growth rate have more effect on consumpton of ol. Populaton s predcted tll 2050 yearly by [U.S. Bureau of the Census. 2004]. But we fal n fndng the predcton of the world economc growth rate, thus, an assumpton s desrable: World economc growth keeps annual growth rate 3.5%. Thus, the fgures are drawn and shown as follows:

9 Team # 8 Page 9 of 29 Fgure 7. Long term Smulaton (wth 5 Years Learnng) The sold-style curve shows the consumpton of ol estmated by our frst model, and the dot-style curve shows the target value estmated by our lnear neural network. The result s not very good, as we gnore some factors. But the trend s approxmately accurate. After all, our neural network model s a lnear functon approxmaton wth the mnmum sum-squared error.[matlab] Thus, wth the weghts and bas determned by the network, we can obtan a functon lke ths : a = w p + b whch approxmately ndcates the relatonshp between the consumpton of ol and the factors. Model for Polcy Evaluaton Gven that each polcy would have an mpact on certan factors and t s dffcult to make the relatonshp clear between them, the quanttatve mpact on ol consumpton s ndescrbable. Our am s to reduce the ol consumpton, and the cost of polcy mplementaton as well as the mantenance should be consdered at the same tme. That means our evaluaton of polcy starts from the pont of economcal. Frst of all, we make two assumptons: We assume the effectveness of each polcy on a certan factor s ndependent. To explan ths assumpton, we take the world economc growth rate as an example. If a polcy reduces the world economc growth rate by %, another polcy reduces t by 2%, the total polcy nfluence on world economc growth rate s ( %)( 2%) = 2.98% We assume the cost of each polcy can be demanded. After that, multple objectve programmng s formulated as below,

10 Team # 8 Page 0 of 29 max mn l = ( j= = n m c n ( + m a j )) p j w j s. t. m = 0, =,2,3, L, n where polcy number =,2,..., n j factor number, j =,2,..., l m whether to adopt the th polcy, f adopted m =, otherwse = 0 m p j value of jth factor n target year w j the weght of the jth factor c the cost of the th polcy a j the mpact of the th polcy on the value of the jth factor To solve ths multple objectve programmng, we have attached great mportance to the reducton of ol consumpton, as t s our man am. We can use the weghted-sums of objectve functons to desgn the best combnaton of polces. In our multple objectve programmng, we gve the two objectves weghtng coeffcents λ ( k =,2) respectvely, and obtan a new objectve functon. (We should make the two objectve functons comparable n advance) k max λ 5 ( ( n j= = 5 n j= = ( + m a ( + a j j )) p )) p j j w w j j λ n = 2 n = m c c s. t m = 0, =,2,3, L, n The decson maker can gve the exact value of each weght coeffcent based on ndvdually actual condtons. Polcy Decson Now we apply our polcy evaluaton model to partcular examples. We just take two polces compared n detal. The frst polcy (FP) taken nto account s to avod overexplotng ol resources. The second polcy (SP) s to ntensfy exploraton and explotaton under the sea.(both of the polces are just our deas, and we beleve these polces can be carred out to reduce the ol

11 Team # 8 Page of 29 consumpton)we am to fnd a better polcy whch has a greater mpact on reducton of ol consumpton, at the same tme wth a smaller cost. We defne several local varables c cost of FP c 2 cost of SP a mpact on th factor of FP ( =,2,3,4,5 ) b mpact on th factor of SP ( =,2,3,4,5 ) m represent whether to select the jth polcy (j=,2). If m =, the jth polcy s adopted, j otherwse t s not put nto practce λ weghtng coeffcent of the goal to maxmze the reducton of ol consumpton j λ 2 weghtng coeffcent of the goal to mnmze the cost of polcy mplementaton Note that: λ + λ 2 = Then we try to assess whch one of the two polces should be selected through our evaluaton model. The objectve programmng s shown below. max λ 5 ( ( + m a )( + m b )) p 2 = λ 5 2 = ( ( + a )( + b )) p w w mc + m2c2 c + c 2 We have known W = m + m2 = s. t. m j = 0, j = 0, ( , 0.009, , , ) corresponds to the populaton, CO2, world economc growth, prce and energy structure respectvely. Then we assume the nfluence coeffcent matrx A s (actually, t can be determned by the method of Analytc Herarchy Process) 0.0 A = and λ = 0. 9, λ = 0. 2 and c = 5 bllon dollars, c = 30 2 bllon dollars (those would be got accordng to the realty easly).

12 Team # 8 Page 2 of 29 Solvng the programmng we get that the optmal soluton s m =, m, 0 2 = whle the value of objectve s So the second polcy s more effectve than the frst one. Management Polcy As the conflct between ol shortage and the ncreasng demand of ol consumpton s becomng more and more acute, how to sustan the usage of ol over a long perod of tme s a hot ssue n the world today. However, severe dsrupton of ol supply s not a satsfed soluton. Because that wll greatly affects people s lves, even makes poltcal stuaton unstable. Hence, we thnk the government should take advantage of economc level fully so as to control the ol consumpton effectually. A package of polces lsted below s our opnons. Assocate ol prce wth the global ol reserves. Ths polcy would make the ol prce rsng along wth ts reserves decreasng. It mposes a heaver and heaver economc pressure on customers. Though t sounds not a delghtful dea, we must mplement the polcy to arouse publc awareness, so as to mtgate the use and potental exhauston of ol. Meanwhle, ths polcy would ensure that the rsng of prce s gradual. So ts mpact to our lves s mnmzed. Popularzaton of energy-savng technologes. Consderng the future energy requrement, energy-savng technologes should be popularzed urgently. Because most of the feasble energy-savng technologes do not have any advantage n prce and performance compared wth exstng technologes, the popularzaton of them faces many economc resstances. Our government should gve specal treatment on these better technologes. So that the pressure brought by ol shortage wll be releved to some extent. Impose approprate consumpton tax of ol. Though ths polcy also aggravate publc burden, we do not have other alternatve but utlze the economc lever to regulate excessve usage of ol. However, we would fund the ncreased money and fnance some organzatons that endeavor to solve energy crss and protect envronment. Intensfyng exploraton and explotaton under the sea. To better solve the conflct between an ncreasng demand and a decreasng supply, we should accelerate the ol exploraton and explotaton under the sea. It s sad mmense reserves of ol keeps offshore. There s 35,000,000,000 tons of ol stored under sea. Ths large ol resource may effectvely releve the shortage of ol supply. Fnance alternatve energy development. Requrement of energy consumpton goes up steadly. Merely savng ol or ncreasng ol explotaton cannot solve the energy crss thoroughly. The crtcal key to ths problem s to fnd alternatve energy that s renewable. Securty Polcy To get through the energy crss steadly, we ought to manage every secton perfectly. Generally speakng, there s a bg deal of losses n the process of explotaton and usage. Thus, we must have some rules to change these stuatons. The followng suggestons are our recommendatons. Collaboraton between developed countres or areas and developng countres or areas.

13 Team # 8 Page 3 of 29 The effcency of energy consumpton s at a low level n developng countres or areas, and the status of wastng energy s serously gong on as well. These stuatons have become a huge burden for those countres or areas, even for the whole world. We must enhance the cooperaton of worldwde. If the poor stuaton of low energy effcency can be mproved, we can save a great deal of ol. Avodng overexplotng ol resources. To use ol more effcently, government should control explotng and refnng ol strctly. The relevant strategy must be gven further thought. Only satsfyng the market requrement blndly and pursung proft recklessly are both shortsghted. That wll deplete the ol reserves very soon and block economc development f such status s stll gong on. It not only damages our envronment, but threatens sustanable development of economcs as well. Furthermore, excessve explotaton would make unnecessary degradaton of the resource. So the government must fnd a reasonable way of explotaton. Any form of overexplotng should be sternly forbdden by law. Update the technology. An advanced technology standard would facltate the development of technology and wash out the outdated technology, whch waste more resource n the process of explotaton and refnng. Furthermore, t can lead a development of energy technology to a promsng way. However, due to the dstrbuton of ol beng so uneven, the ol transportaton s frequently and crucally for each country. Thus we should take a great care of the securty of transportaton equpments, such as ol ppelne and ol carryng vessels. They have become targets of terrorst. And the securty of ol ppelne especally n those unstable areas has been threatened. As well as there s a great possblty that the terrorsts would attack the sea transportaton of ol exsts. If such case happened, the ol leakage wll make great destroy n envronment effect and resource dsrupton. Thus a securty strategy for ol transport s crtcal for all the countres. There are several long-term ol securty polces we have consdered: Provde varous transportaton of ol. Enhance vglance of publc on transportaton securty. Increase budget for securty of energy transportaton. Strengthen cooperaton between dfferent countres or dfferent areas. Polcy related to Control Envronmental Effects of the Ol Harvestng As everyone knows, ol spll polluton and Carbon Doxde emssons are the bggest Ol-related envronment problems. Accdental ol polluton from tanker has shocked the world snce 978, although large tanker ol splls have been effectvely controlled by now.

14 Team # 8 Page 4 of 29 Fgure 8: Tanker ncdent by cause [INTERTANKO, 2004] As shown above, tanker ncdent has been sgnfcantly reduced. And INTERTANKO clamed that n 2003, there s only 0.003% of the world seaborne ol spllng nto the sea. Nevertheless, we stll concern the polluton of ol spllng, and polces to control t are stll needed. We fnd that n the 20 largest tanker ol spll events, 80% happened on the tanker whose ownershp s ndependent, and over half of the tankers are over 5 years old. So, we suggest that the tanker over 5 years old should be examned more carefully, especally to the tanker whose ownershp s ndependent. Table 4: Ol Polluton. The 20 Largest Tanker Ol Splls [INTERTANKO, 2004] Another ol-related envronment problem s ar polluton. Carbon doxde emssons, the largest contrbutor to greenhouse gas emsson worldwde, are largely due to the combuston of fossl fuels n electrc power generaton, motor vehcles, and ndustres. So more and more actons that lmt the usage of ol have been taken. Consderng a natonal lmt on the release of carbon doxde, an nternatonal effort s

15 Team # 8 Page 5 of 29 necessary to tackle the problem. And our vewpont s as follows:. Energy effcency s seen as the most economcally advantageous and fastest route to curbng emssons of carbon doxde and abandonng fast reactor technology research s shortsghted. 2. The contrbuton of transport fuel emssons to the greenhouse effect should be consdered 3. Methane from ol felds should be collected or dsposed. Outlne of Other Promsng Alternatves and Relevant Encouraged Polcy What are the alternatves for replacng ol? Ths queston receves more and more attenton along wth the ol runnng out. One way of lookng at the future of world energy s to consder technology trends n the consumpton. And now some progresses have been acheved. Coal. Through coal s not a new resource and nonrenewable, ts huge reserves and avalable technology make t a compettve canddate for substtutng ol. The followng two tables show ts reserves and energy. From both of them, we can easly reveal that the coal could sustan a long perod energy requrement. However the emssons CO 2 of coal cannot be neglected. Because there s one day the coal exhaustble eventually, we just can use coal as a transton but not rely on for a long term. Table 5: World Coal Resources and Products (Tons Mllons) [Denns L. Yakobson, 2004,5] RESERVES ANNUAL PRODUCTION USA Chna Inda 275,600 26,200 93,000,2, Sum Rest of World Total 494, ,600,085,400 2,99 2,347 5,266 Table 6: Advanced Clean Coal Potental Impact [Denns L. Yakobson, 2004,5] USA Chna Inda Ggawatts Tons Coal per Day 670,000,440, ,000 Conventonal Coal CO 2 (tons/day).7 mllon 3.7 mllon 0.8 mllon Wnd Energy. It s a clean and renewable resource that has not been wdely used n the world. But t s sad the world's fastest growng energy source. Up to 2002, Because of low nstalled-cost, Cumulatve global wnd energy generatng capacty topped 3,000 megawatts (MW). [Amercan Wnd Energy Assocaton, 2003] And the capacty greatly enhances every year. That has been shown n the Fgure X

16 Team # 8 Page 6 of 29 Fgure 9: Increase of Global Wnd Energy Capacty [Amercan Wnd Energy Assocaton, 2003] Tdal energy. Tdal energy s a clean and non-solar renewable knd of energy. Some stes have the potental to produce as much electrcty as several large conventonal power statons. The estmated reserves are 3,000,000,000KW. However, only n the stes where tdes can be concentrated and amplfed can we utlze ths energy. Thus, the tdal energy has some defnte lmtaton. Besdes the above alternatves, there are stll some feasble technologes. The followng table roughly descrbe ther actualtes. Table 7: Other Promsng Technologes [Unted Natons Development Programme, 2004] a. Heat emboded n steam, often produced n combned heat and power systems. b. Small hydro s usually defned as 0 MW or less, although the defnton vares by country, sometmes extendng to 30 MW. Note: Modern bomass contrbuted 7 exajoules and other new renewables contrbuted 2 exajoules n 998 Through the outlne of varous alternatves descrbed above, we fnd out our future energy crss can be solved f we take correct countermeasures. After all, the explotaton of new energy and technologes s the thorough ways to smooth ths urgent crss. Thus, we should consttute some polces to actvate the research

17 Team # 8 Page 7 of 29 progress. The followng tems are our proposals. Take advantage of publc fundng more effectvely. We should not dstrbute lmted fund to every corner of technology, at least emphasze partcularly on several most promsng felds such as Formulate several pror teams on each promsng feld. Even n the same feld, the progress of dfferent teams may be at dfferent level. Am to accelerate the progress and avod wastng money on redundant work, our government should brng up some excellent teams. Rasng publc awareness. Any technology popularzaton needs publc support. Sometmes, t even makes more expense of people. We should rase ther anxety for energy crss. So that the feasble technologes could be really appled. Strengthen cooperaton between research nsttuton and ndustres. Both of them are the most prmary power for technology development. The collaboraton between them can exchange more knowledge and nformaton so as to advance the technology. Accelerate the transform from research achevement to applcable product. In some cases, many mature technologes are blocked from market by varous reasons. We should mprove relevant systems to assure and cut the tme of transform. Strengths and Weaknesses Through our selecton of nputs s subjectve, the model can judge sutablty of nputs ntellgently. By calculatng the dfferent weght of nputs, our model s able to correct the nfluence brought by mproper factors. Ths strength of our model s very mportant. After all, we cannot make sure that all of the selectve factors are effectve n projecton for ol consumpton and non-selectve ones play unmportant roles n ths projecton. Now we would llustrate an example to explan ths strength. Through the weghts of neurons solved, we can fnd the weght of ol prce s much smaller than the others. Why s ths? Intutvely, the ol prce affect even most sgnfcantly on ol consumpton. However, the ol prce has been determned by so many terms especally some poltcal and ncdental ones. For nstance, OPEC polces and Iraq Wars can nfluence the ol prce greatly. We can see the acute fluctuaton of ol prce from the followng fgure Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Fgure 0. Prce of Ol from 990 to 2005 Thus due to the ol prce determned by so many uncertan factors, we should

18 Team # 8 Page 8 of 29 gnore ts affect on projecton for ol consumpton. So our model dstrbutes ths neuron (ol prce) a very small weght coeffcent and forecast the ol consumpton nearly gnore ts nfluence. Moreover, the neuron network has a wdely applcaton Our model has a lmtaton that the lnear neural network used n our model 2 s just a functon approxmaton. Second Choce of Model Buldng The Analytc Herarchy Process (AHP) The Analytc Herarchy Process (AHP) s a powerful and flexble decson makng process to help people set prortes and make the best decson when both qualtatve and quanttatve aspects of a decson need to be consdered. [Expert Choce, 2005] In ths problem, AHP can be used to wegh the nfluence of each factor on ol consumpton. world economc growth rate, world populaton, ol prce, emsson of carbon doxde and energy structure are regarded as the determnng factor on yearly ol consumpton. As developed countres and developng countres have dfferent mpact on the fve factors, we develop the herarchy framework and t s lsted below. Fgure. Herarchy Structure of Ol Consumpton The process requres the analyst (decson maker) to make parwse comparsons of elements at each level relatve to each actvty at the next hgher level n the herarchy. For example, we parwse compare the weght of world economc growth rate, world populaton, ol prce, emsson of carbon doxde and proporton of ol consumpton n energy consumpton to developed countres. We assume the weght of world economc growth rate on developed countres s ω, and the weght of world populaton on developed countres s ω 2, and so on. We use ω to represent the ω 2 rato of the nfluence of world economc growth rate on developed countres and world populaton on developed countres. Then the judgment matrx W s ganed. The result s

19 Team # 8 Page 9 of 29 ω ω ω ω ω2 ω3 ω4 ω5 ω 2 ω2 ω2 ω2 ω ω3 ω4 ω5 ω = 3 ω3 ω 3 ω W 3 ω ω2 ω4 ω5 ω4 ω4 ω4 ω 4 ω ω2 ω3 ω5 ω5 ω5 ω5 ω5 ω ω2 ω3 ω4 By such parwse comparson at each level throughout the herarchy, we can get other judgng matrxes. However, to get the exact number, the 9-pont scale whch s the standard ratng system used n AHP need to be used. Ths determnaton wll be based on the judgment/experence of the decson maker. The process gves the data for further determnaton and t s crucal n the further analyss. It s recommended that experts or professonals wth a wealth of knowledge and good judgment complete ths process. That s why we fnally do not adopt ths method to develop our model. However, for nternatonal organzatons or governments, there may be enough experts to determne the relatve prorty of each attrbute to each attrbute one level up n the herarchy. Thus ths method wll help arrve at the best decson and s a good choce to acheve our goal. Reference Energy Informaton Admnstraton Internatonal Energy Outlook Energy Informaton Admnstraton Internatonal Energy Annual Energy Informaton Admnstraton U.S. Petroleum Prces. FECIT nsttute MATLAB 6.5 assstant analyss and desgn n Neural Network. Publshng House of Electroncs Industry (Chna). Australa Government The Commonwealth Budget - Overvew U.S. Bureau of the Census Total Mdyear Populaton for the World: Texas Instruments Incorporated The World Populaton: Logstc Model. MATLAB6p help/techdoc/math_anal/poly_4.html Denns L. Yakobson. 2004,5. A Domestc F-T Desel Alternatve. Amercan Wnd Energy Assocaton Global Wnd Energy Market Report Expert Choce Inc Thomas B. Johansson and José Goldemberg Energy for Sustanable Development. Unted Natons Development Programme Energy for Sustanable Development.

20 Team # 8 Page 20 of INTERTANKO Tanker Facts Appendx (Thousand Barrels per Day) North Amerca Table : World Petroleum Consumpton, [Energy Informaton Admnstraton, 2004] Central & South Amerca Western Europe Eastern Europe & Former U.S.S.R. Mddle East Afrca Asa & Oceana Total consumpt on Table 2. World Prmary Energy Consumpton by Energy Source [Energy Informaton Admnstraton. 2004] Ol Natural Gas Coal Nuclear Other

21 Team # 8 Page 2 of Fgure. World Annual Economc Growth Table 3. All Countres Spot Prce FOB Weghted by Estmated Export Volume [Energy Informaton Admnstraton. 2004] ($/bbl) Jan 05, Jan 04, Jan 03, Jan 0, Dec 7, Jan 07, Jan 06, Feb 24, Jan 05, Apr 2, Jan 03, Jan 0, Jan 7, Apr, Apr 8, Apr 25, May 02, May 09, May 6, May 23, May 30, Jun 06, Jun 3, Jun 20, Jun 27, Jul 04, Sep 26, Oct 03, Oct 0, Oct 7, Oct 24, Oct 3, Nov 07, Nov 4, Nov 2, Nov 28, Dec 05, Dec 2, Dec 9, Mar 3, Mar 20, Mar 27, Apr 03, Apr 0, Apr 7, Apr 24, May 0, May 08, May 5, May 22, May 29, Jun 05, Jan 24, Jul, Dec 26, Jun 2, 998. Jan 3, Feb 07, Feb 4, Feb 2, Feb 28, Mar 07, Mar 4, Mar 2, Mar 28, Apr 04, Jul 8, Jul 25, Aug 0, Aug 08, Aug 5, Aug 22, Aug 29, Sep 05, Sep 2, Sep 9, Jan 02, Jan 09, Jan 6, Jan 23, Jan 30, Feb 06, Feb 3, Feb 20, Feb 27, Mar 06, Jun 9, Jun 26, Jul 03, Jul 0, Jul 7, 998. Jul 24, Jul 3, Aug 07, Aug 4, Aug 2,

22 Team # 8 Page 22 of 29 Aug 28, Jun 25, Apr 2, Feb 6, Sep 04, Jul 02, Apr 28, Feb 23, Sep, Jul 09, May 05, Mar 02, Sep 8, Jul 6, May 2, Mar 09, Sep 25, Jul 23, May 9, Mar 6, Oct 02, Jul 30, May 26, Mar 23, Oct 09, Aug 06, Jun 02, Mar 30, Oct 6, Aug 3, Jun 09, Apr 06, Oct 23, Aug 20, Jun 6, Apr 3, Oct 30, Aug 27, Jun 23, Apr 20, Nov 06, Sep 03, Jun 30, Apr 27, Nov 3, Sep 0, Jul 07, May 04, Nov 20, Sep 7, Jul 4, May, Nov 27, Sep 24, Jul 2, May 8, Dec 04, Oct 0, Jul 28, May 25, Dec, Oct 08, Aug 04, Jun 0, Dec 8, Oct 5, Aug, Jun 08, Dec 25, Oct 22, Aug 8, Jun 5, Jan 0, Oct 29, Aug 25, Jun 22, Jan 08, Nov 05, Sep 0, Jun 29, Jan 5, Nov 2, Sep 08, Jul 06, Jan 22, Nov 9, Sep 5, Jul 3, Jan 29, Nov 26, Sep 22, Jul 20, Feb 05, Dec 03, Sep 29, Jul 27, Feb 2, Dec 0, Oct 06, Aug 03, Feb 9, Dec 7, Oct 3, Aug 0, Feb 26, Dec 24, Oct 20, Aug 7, Mar 05, Dec 3, Oct 27, Aug 24, Mar 2, Jan 07, Nov 03, Aug 3, Mar 9, Jan 4, Nov 0, Sep 07, Mar 26, Jan 2, Nov 7, Sep 4, Apr 02, Jan 28, Nov 24, Sep 2, Apr 09, Feb 04, Dec 0, Sep 28, Apr 6, Feb, Dec 08, Oct 05, Apr 23, Feb 8, Dec 5, Oct 2, Apr 30, Feb 25, Dec 22, Oct 9, May 07, Mar 03, Dec 29, Oct 26, May 4, Mar 0, Jan 05, Nov 02, May 2, Mar 7, Jan 2, Nov 09, May 28, Mar 24, Jan 9, Nov 6, Jun 04, Mar 3, Jan 26, Nov 23, Jun, Apr 07, Feb 02, Nov 30, Jun 8, Apr 4, Feb 09, Dec 07,

23 Team # 8 Page 23 of 29 Dec 4, Oct 04, Jul 25, May 4, Dec 2, Oct, Aug 0, May 2, Dec 28, Oct 8, Aug 08, May 28, Jan 04, Oct 25, Aug 5, Jun 04, Jan, Nov 0, Aug 22, Jun, Jan 8, Nov 08, Aug 29, Jun 8, Jan 25, Nov 5, Sep 05, Jun 25, Feb 0, Nov 22, Sep 2, Jul 02, Feb 08, Nov 29, Sep 9, Jul 09, Feb 5, Dec 06, Sep 26, Jul 6, Feb 22, Dec 3, Oct 03, Jul 23, Mar 0, Dec 20, Oct 0, Jul 30, Mar 08, Dec 27, Oct 7, Aug 06, Mar 5, Jan 03, Oct 24, Aug 3, Mar 22, Jan 0, Oct 3, Aug 20, Mar 29, Jan 7, Nov 07, Aug 27, Apr 05, Jan 24, Nov 4, Sep 03, Apr 2, Jan 3, Nov 2, Sep 0, Apr 9, Feb 07, Nov 28, Sep 7, Apr 26, Feb 4, Dec 05, Sep 24, May 03, Feb 2, Dec 2, Oct 0, May 0, Feb 28, Dec 9, Oct 08, May 7, Mar 07, Dec 26, Oct 5, May 24, Mar 4, Jan 02, Oct 22, May 3, Mar 2, Jan 09, Oct 29, Jun 07, Mar 28, Jan 6, Nov 05, Jun 4, Apr 04, Jan 23, Nov 2, Jun 2, Apr, Jan 30, Nov 9, Jun 28, Apr 8, Feb 06, Nov 26, Jul 05, Apr 25, Feb 3, Dec 03, Jul 2, May 02, Feb 20, Dec 0, Jul 9, May 09, Feb 27, Dec 7, Jul 26, May 6, Mar 05, Dec 24, Aug 02, May 23, Mar 2, Dec 3, Aug 09, May 30, Mar 9, Jan 07, Aug 6, Jun 06, Mar 26, Jan 4, Aug 23, Jun 3, Apr 02, Jan 2, Aug 30, Jun 20, Apr 09, Jan 28, Sep 06, Jun 27, Apr 6, Sep 3, Jul 04, Apr 23, Sep 20, Jul, Apr 30, Sep 27, Jul 8, May 07,

24 Team # 8 Page 24 of 29 Table 4. World Energy-Related Carbon Doxde Emssons by Fuel Type [Energy Informaton Admnstraton. 2004] Year Amount Year Amount ,28,653, ,365,480, ,449,369, ,53,04, ,6,269, ,69,759, ,770,70, ,849,885, ,927,556, ,004,70, ,079,603, ,53,80, ,226,933, ,299,763, ,372,797, ,446,3, ,59,645, ,593,36, ,667,272, ,74,49, ,85,892, ,890,482, ,965,84, ,039,69, ,3,740, ,87,084, ,259,678, ,33,553, ,402,60, ,472,7, ,54,773, ,609,796, ,676,807, ,742,728, ,807,5, ,87,43, ,933,735,688 Table 5. Total Mdyear Populaton for the World: [U.S. Bureau of the Census. 2004] ,995,46, ,056,200, ,6,088, ,75,075, ,233,237, ,290,662, ,347,339, ,403,243, ,458,343, ,52,68, ,566,305, ,69,93, ,67,39, ,722,646, ,773,94, ,822,987, ,87,973, ,920,03, ,967,336, ,03,687, ,059,76, ,03,776, ,47,470, ,90,252,532

25 Team # 8 Page 25 of 29 The programme of lnear neural network to predct future ol consumpton. [MATLAB 6.5] ol.m functon net=ol(year,year2) LearnYear=max([mn([year,2005]),990]); TestYear=max([mn([year2,2005]),990]); Tme=nlne('990:0.:t'); Populaton=[990: ]; CO2=[990: ; ]; WEG=[990:2050; dff(33.58*(+0.035).^[:( )])]; Prce=[ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,...

26 Team # 8 Page 26 of , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,...