Safety Evaluation Model of Chemical Logistics Park Operation Based on Back Propagation Neural Network

Transcription

1 1513 A pubication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 6, 017 Guest Editors: Fei Song, Haibo Wang, Fang He Copyright 017, AIDIC Servizi S.r.. ISBN ; ISSN The Itaian Association of Chemica Engineering Onine at DOI: /CET17653 Yi Ban Safety Evauation Mode of Chemica Logistics Park Operation Based on Back Propagation Neura Network Xi'an Aeronautica University, Xi'an , China @16.com In order to study the chemica ogistics park operation safety evauation mode based on BP neura network, this paper firsty introduces the earning agorithm of BP neura network and evauation method of BP neura network. Then, from the viewpoint of chemica ogistics park operation safety infuence factors, we estabish the chemica ogistics park operation safety evauation index system. In addition, a safety evauation mode of chemica ogistics park based on neura network is constructed, and the network mode is trained and simuated by using the neura network toobox graphica user interface. Finay, it is proved that the error of the mode reaches the predetermined range and has certain practicabiity. Thus, it is concuded that the safety evauation mode has quite good performance in appication in chemica ogistic park. 1. Introduction The deveopment of chemica industry park in China is rapid in recent years, and at present, there are many parks with more mature deveopment. Whereas, affected by the centra and western regions cose to raw materias, ow abor costs and other factors, the trend of chemica industria park graduay deveoping from the eastern coasta areas to the centra and western regions is obvious (Ren et a., 014). With the deveopment of the chemica industry and market division, it is taken into account that the chemica ogistics operation is compex, equipment is speciaized and safety requirements are higher and other characteristics, so internationa production type chemica enterprise itsef rarey engaged in chemica ogistics. Instead, the ogistics business is outsourced to speciaized third party ogistics service provider, which makes the chemica ogistics rapidy deveop. And then chemica ogistics park provides supporting ogistics service for the chemica industry, such as warehousing, transportation, packaging business of chemica raw materias and products (Zhang et a., 015). As China's chemica industry park presents the scaed and intensive deveopment trend, with the emergence of a series of chemica industria custers, athough many regions have introduced chemica ogistics park construction panning, few studies were carried out on the operation mode and operation safety evauation of chemica ogistics park, thus acking perfect theoretica as the guidance. To reaize the efficient and safe operation of chemica ogistics park (Sun et a., 014) has become the key to fuy reaize the scae benefit of chemica industry custer. In this paper, on the basis of existing research resuts, combined with the actua situation of chemica ogistics park, the chemica ogistics park operation safety evauation mode is constructed by using neura network, to make an evauation and anaysis of factors affecting the operation safety, so as to provide a reference for improving the park operation safety eve. Through anayzing the existing iterature, the ogistics operation mode and chemica ogistics park ayout mode firsty in this paper, the chemica ogistics park operation mode and chemica ogistics park operation safety evauation index system are put forward (Wang et a., 017). Then, by constructing a neura network evauation mode, the chemica ogistics park operation safety is evauated. Finay, an exampe is given to iustrate the effectiveness and operabiity of the proposed mode. Pease cite this artice as: Yi Ban, 017, Safety evauation mode of chemica ogistics park operation based on back propagation neura network, Chemica Engineering Transactions, 6, DOI: /CET17653

2 1514. Method BP network is back propagation network. And BP neura network is a widey used neura network mode at present. The essence is to adust the weights of each ayer so that it can earn and memorize the earning sampe set. The training process consists of two parts: forward process cacuation node error and reverse process adustment weight. BP network is generay composed of 3 neuron ayers, namey input ayer, hidden ayer and output ayer. A the processing units of each ayer form fu interconnection connection (Zhong et a., 016), and there is no connection between the processing units in the same ayer. The transfer function of each ayer is sigmoid type function..1 BP neura network evauation method BP network is a one-way propagation mutiayer feedforward neura network, which is simiar to mutiayer perceptron in structure. The BP network was proposed by scientists in BP networks are widey used in the fied of science and technoogy (Yune et a., 016). on the basis of fuy considering the difficuty understanding degree of the current artificia neura network agorithm, the BP network is seected as the evauation mode. It has the advantages of simpe structure, adustabe parameters, various training agorithms, good maneuverabiity and so on advantages, and it can achieve a good risk assessment for high speed raiway. The BP network mode is shown in Figure 1. BP network has three or more than three ayers neurons, incuding input ayer, hidden ayer and output ayer (Lee et a., 016). The hidden ayer can be a singe ayer or a mutiayer. The neurons between the adacent ayers are fuy connected, and there is no connection between the neurons in each ayer. Figure 1: Neura network mode The basic principe of BP network is that after a set of input vectors are suppied to the network, the neurons are stimuated, and the activation vaues propagate through the intermediate ayer to the output ayer. The output ayer nodes respond to the stimuus to get the corresponding output, and compete a forward propagation (Xing, 016). Then, the error of the actua output and expected output of the system is anayzed, and through the output ayer, is passed through each intermediate ayer according to the error reduction direction, and the weights and threshods are adusted unti the input ayer, so as to compete the back propagation, that is, BP agorithm (Rami et a., 014). With the adustment of weights and threshods, the error between the actua output and the expected output is reduced unti the error reaches a predetermined range, and then the earning is stopped. The whoe process reaizes a mapping reationship between input data and output data.. Learning agorithm of BP neura network The principe of BP neura network mode invoves many mathematica formuas, parameters and operation rues (Zhang et a., 014). In order to faciitate the understanding of basic ogic thinking of the neura network mode in the process of anayzing and evauating the research obect, it needs to briefy understand the agorithm fow of BP neura network mode. The BP agorithm is based on gradient descent. By adusting weights and threshods, the mean square error between the output expectation vaue and the actua output vaue of the neura network tends to be the minimum. The schematic diagram of the BP agorithm is shown in Figure.

3 1515 Figure : Schematic diagram of agorithm This section takes the three-ayer BP network as an exampe to introduce the earning process of BP network. The input node is x i, the hidden ayer node is y, and the output node is z. The connection weight between input node and hidden ayer node is w i, and the connection weight between hidden ayer node and output node is v i. The threshod of each neuron in the hidden ayer is θ, and the threshod of each neuron in the output ayer is θ. The excitation functions of the hidden ayer and the output ayer are f. The expected output of the output node is t. The cacuation formua of BP network mode is as foows: The output of hidden ayer nodes: y = f ( w x θ ) = f ( net ) (1) i The output of output ayer nodes: z f ( v y θ ) = f ( net ) () = Mean square error function between actua output and expected output: 1 1 E = ( t z ) = ( t f ( v f ( w x θ)) (3) i Weight modification between input ayer and hidden ayer node: w ( k + 1 ) = w ( k) + Δw = w ( k) + η' δ x (4) i From the above formuas, it is known that, the error representing the output node in the hidden ayer node error is back transmitted to the error of the hidden ayer node through the weight. In order to describe the impementation process of the agorithm, the fow chart is used to represent the agorithm, as shown in Figure 3. Figure 3: Fow chart of agorithm

4 Construction of operation safety evauation mode for chemica ogistics park 3.1 Appication of BP neura network mode Chemica ogistics park is a new park form (Shao et a., 013). The current domestic schoars made itte research on it (Thompson et a., 013). The evauation invoving the operation safety aspects has no historica data for being used, so the expert scoring is used to obtain the sampe data of chemica ogistics park operation safety evauation index system. In order to prevent part of the neurons reaching the saturation, the sampe data shoud as far as possibe in the interva. As a resut, the operation safety evauation grade is divided into exceent, good, moderate, and poor. The better the operation safety is, the higher the corresponding numerica score is. In this paper, through the expert scoring form for expert survey to score, we obtained the group sampe data, as shown in tabe 1. Tabe 1: Chemica ogistics park operation safety evauation sampe data U U U U U U U U U U According to the weight vaue of each index, the 14 groups of sampe data were weighted and cacuated, and the resuts were shown in tabe. Tabe : Chemica Logistics Park operation safety evauation score tabe Sampe Score Sampe Score Through the previous anaysis, the number of neurons in the input ayer is determined, and the number of neurons in the output ayer is known. The number of neurons in the hidden ayer is determined according to the formua. On this basis, the neurons in the hidden ayer are continuousy increased and reduced. The number of neurons in the hidden ayer is determined by the seection, and the satisfactory seection is taken. Therefore, the network structure of safety assessment mode of chemica ogistics park based on neura network is shown in Figure 4. The first groups of sampe data in a the sampe data are taken as training sampes, and each index in the evauation index system of sampe is taken as input data. The weighted mean of each sampe data is desired as output data, and a BP neura network is created for training by MATLAB neura network toobox CUI. In order to make the sampe data get better training, we use the dynamic gradient descent agorithm with adaptive earning rate, momentum back propagation and dynamic adaptive earning rate, BP agorithm of Levenberg_Marquardt three training function, and for each training function, we adopt the tansig and odsig two kinds of transfer functions for the comparative anaysis. The training resuts of parameter setting network are anayzed and compared, and the concusion are as foows: On the whoe, the convergence rate of trainm training function is the fastest, and ony the network with trainm training function can achieve the high matching of training sampes. For each ayer transfer function, the odsig function converges faster than the tansig function, and the simuation error is the minimum. Therefore, after comprehensive consideration, this paper uses tansig function and odsig function, respectivey, in training safety function and transfer function of each ayer in the operation safety evauation of chemica ogistics park.

5 1517 Figure 4: Network structure diagram of operation safety evauation mode of chemica ogistics park based on Neura Network 3. Simuation of network mode In the sampe data, the atter 6 sets of sampe data n and the corresponding data m cacuated by weight are used as input data and expected output data of mode simuation. After competing the training of the sixth parameter setting network, cick "Simuate" in "Network:BP", and simuate with the input data n in the trained network. The output variabe of the simuation is sim_outputs, the simuation error is sim_errors, and the specific simuation resuts are compared with the expected resuts, as shown in tabe 3. Tabe 3: Comparison tabe of neura network simuation output and expected output: Seria number Seria number Actua output Error The error variation curve is obtained, as shown in Figure Figure 5: Curve of error variation

6 1518 We found that by simuation resuts in Figure 5, the error of the actua output and the expected output is very sma, which shows that the chemica ogistics park operation safety evauation mode estabished by neura network is effective. 4. Concusion In this paper, the chemica ogistics park operation safety evauation is studied based on BP neura network mode. First of a, from the characteristics of three kinds of chemica ogistics park ayout modes and its appicabe conditions, combined with different characteristics of chemica ogistics park and the specific needs of enterprise, we provide the basis for arrangement of proper panning of ogistics park infrastructure faciities ayout. According to the chemica ogistics park operation safety evauation index system, we construct the chemica ogistics park operation safety evauation mode based on BP neura network, and divide the operationa safety into exceent, good, moderate and poor four eves. Then, MATLAB neura network toobox graphica user interface is appied in network training and simuation, so the error of the mode reaches a predetermined range. However, due to the space probem, this paper acks the actua case to compare the security of the typica operation mode. Reference Lee Q., Lihua, K., 016, Technica framework design of safety production information management patform for chemica industria parks based on coud computing and the Internet of things. Internationa Journa of Grid and Distributed Computing, 9, , DOI: /igdc Rami A., Mokhtar M., Aziz B.A., Ngah, N.A., 014, The cooperative approach in managing safety issues for Haa industria parks in Maaysia: embracing opportunity. Progress in Industria Ecoogy, an Internationa Journa, 8, , DOI: /pie Ren C., An N., Wang J., Li L., Hu B., Shang D., 014, Optima parameters seection for BP neura network based on partice swarm optimization: A case study of wind speed forecasting. Knowedge-Based Systems, 56, 6-39, DOI: /.knosys Shao C., Yang J., Tian X., Ju M., Huang L., 013, Integrated environmenta risk assessment and whoeprocess management system in chemica industry parks. Internationa ourna of environmenta research and pubic heath, 10, , DOI: /ierph Sun Z., Wang X., Zhang J., Yang, H., 014, Prediction and contro of equiaxed α in near-β forging of TA15 Tiaoy based on BP neura network: For purpose of tri-moda microstructure. Materias Science and Engineering: A, 591, 18-5, DOI: /.msea Thompson D.N., Campbe T., Bas B., Runge T., Teymouri F., Ovard, L.P., 013, Chemica preconversion: appication of ow-severity pretreatment chemistries for commoditization of ignoceuosic feedstock. Biofues, 4, , DOI: /bfs Wang X., Zhang F., Ding J., Latif A., Johnson V.C., 017, Estimation of soi sat content (SSC) in the Ebinur Lake Wetand Nationa Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neura network mode and optima spectra indices. Science of The Tota Environment, 615, , DOI: /.scitotenv Xing L.I.U., 016. Study on externa safety protection distance for storage area of dangerous goods in ogistics base. Journa of Safety Science and Technoogy, 6(13), DOI: /wicom Yune J.H., Tian J., Liu W., Chen L., Descamps-Large C., 016, Greening Chinese chemica industria park by impementing industria ecoogy strategies: A case study. Resources, Conservation and Recycing, 11, 54-64, DOI: /.resconrec Zhang J., Zhao X., Liu X., Hou, L., 014, Framework Design of Emergency Management Information System for Coud Computing in Chemica Park. CIT. Journa of Computing and Information Technoogy,, 75-83, DOI: /cit Zhang Y., Gao X., Katayama, S., 015, Wed appearance prediction with BP neura network improved by genetic agorithm during disk aser weding. Journa of Manufacturing Systems, 34, 53-59, DOI: /.msy Zhong Z., Tang S., Peng G., Zhang Y., 016, A nove quantitative spectra anaysis method based on parae BP neura network for dissoved gas in transformer oi. In Power and Energy Engineering Conference (APPEEC), IEEE PES Asia-Pacific, 1, , DOI: /appeec