Performance evaluation of lean sustainable systems using adaptive neuro fuzzy inference system: a case study

The modern manufacturing organisations have been adopting both lean and sustainable manufacturing paradigms to survive in the competitive environment. Lean and sustainable strategies aim at achieving productivity improvement by streamlined processes and waste elimination. The performance of such integrated lean sustainable system has to be assessed to ensure whether the strategic objectives are in line with customer value and to identify improvement opportunities that enable and enhance the competitive advantage of an integrated lean sustainable system. This paper presents the formulation of conceptual performance evaluation model to assess lean sustainable systems. Appropriate performance indicators have been identified and adaptive neuro fuzzy inference system approach is used to evaluate lean sustainability. A case of automotive component manufacturing firm in Indian scenario is exemplified. Based on the evaluation, lean sustainability index was computed and the firm was found to be ‘Averagely Lean Sustainable’. Further improvement actions to enhance the lean sustainable performance were planned and implemented. The developed performance evaluation model is capable of effectively evaluating the lean sustainable performance of manufacturing firms and has more practical relevance.     

A neuro-fuzzy algorithm for assessment of health,safety, environment and ergonomics in a large petrochemical plant

Process plants such as petrochemical units have been continuously trying to improve Health, Safety, Environment and Ergonomics (HSEE) programs. This study proposes an adaptive network-based fuzzy inference system (ANFIS) for assessment of HSEE programs in a petrochemical plant. The proposed neuro-fuzzy approach is applied to a set of operators in the petrochemical unit to show its applicability and superiority. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and accomplishment of HSEE programs is used as output for the algorithm. Moreover, this algorithm is used to rank operators performance with respect to HSEE. Finally, the algorithm identifies efficient operators with respect to HSEE. This is the first study that introduces an intelligence algorithm for assessment and improvement of HSEE program in a petrochemical plant.     

A flexible intelligent algorithm for identification of optimum mix of demographic variables for integrated HSEE-ISO systems: The case of a gas transmission refinery

This study proposes a flexible intelligent algorithm for assessment and optimization of demographic features on integrated health, safety, and environment and ergonomics (HSEE)-ISO systems among operators of a gas transmission refinery. To achieve the objectives of this study, standard questionnaires with respect to HSEE and ISO standards are completed by 80 operators. Demographic features include age, education, gender, weight, stature, marital status, and work type. The average results for each category of HSEE are used as inputs and effectiveness of ISO systems (ISO 18000, ISO 14000, and ISO 9000) are used as output for the intelligent algorithm. Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) in addition to conventional regression are used in this paper. Result shows the applicability and superiority of the flexible intelligent algorithm over conventional methods through mean absolute percentage error (MAPE). Computational results show that the proposed ANN performs better than ANFIS and conventional regressions based on its relative error. Finally, the optimum mix of demographic variables from viewpoint of HSEE and ISO are identified. This is the first study that proposes a flexible intelligent algorithm for assessment of optimum mix of demographic features for HSEE and ISO systems in a complex system such as a gas transmission refinery.     

基于模糊神经网络的A2/O工艺出水氨氮在线预测模型

采用厌氧/缺氧/好氧污水处理系统(A2/O)对人工合成污水进行处理,并利用人工神经网络(ANN)模型和自适应模糊人工神经网络(ANFIS)模型对A2/O处理污水的过程进行仿真模拟.在MATLAB环境下,选取可在线监测的水力停留时间(HRT)、进水pH值(pH)、好氧池溶解氧(DO)和混合液回流比(r)作为输入参量,系统出水氨氮浓度(NH4+eff)为输出量,建立在线预测模型.结合自适应模糊C均值聚类算法,确定ANFIS模型的模糊规则数及最优运行参数,对实验数据进行仿真预测.结果表明,与ANN模型相比,ANFIS模型的仿真输出值与实际值的拟合程度更高,相对误差在6.45%之内,平均绝对百分比误差(MAPE)为2.8%,均方根误差(RMSE)为0.1209,相关系数(R)达0.9956.模型训练过程中所得到的三维曲面图,可直观的反映各因素与出水氨氮浓度之间的非线性函数关系,为A2/O系统的高效稳定运行提供指导.     

基于ANFIS的陕西省参考作物蒸散量计算

为有效提高陕西省参考作物蒸散量（ET₀）的计算精度,选取陕西省6个气象站点57 a（1960—2016年）每日气象资料,构建8种基于自适应神经模糊推理系统（ANFIS）的ET₀计算模型,将其与Hargreaves-Samani、Makkink和Iramk等三种在陕西省ET₀计算精度较高的模型进行比较。结果表明：ANFIS模型能较好地反映气象因子与ET₀之间的复杂非线性关系,在仅有气温数据时,ANFIS模型具有足够的精度（平均R²=0.894,平均R_MSE=0.558 mm/d,平均MRE=18.258%）,ANFIS模型的精度随着气象因子数量的增加而增加;在相同气象条件下,ANFIS模型模拟效果最好;ANFIS模型具有较强的泛化能力,基于不同站点也有较高的精度（平均R²=0.974,平均R_MSE=0.276 mm/d,平均MRE=8.608%）,具有很好的可移植性。因此,在缺少气象数据时,ANFIS可以作为陕西省ET₀的计算模型,为农业用水管理及水资源优化配置提供帮助。     

基于ANFIS改进的大气腐蚀环境缺失数据填补方法

目的针对大气腐蚀中重要环境数据缺失的复杂问题,提出一种相关因素(Relevance Factors)和自适应神经模糊推理系统(Adaptive Neuro-Fuzzy Inference System)结合的方法(RF-ANFIS)对缺失数据进行填补。方法首先采用相关因素方法计算缺失数据和多项环境因素间的相关程度,筛选出相关系数较大的因子,然后应用ANFIS构建缺失数据与所选环境因子的关系模型。最后以二氧化硫数据为具体对象,采用北京2015年的气象数据对所建立的模型进行检验。结果经过改进的RF-ANFIS模型在最优情况下样本均方误差为0.696,在14个测试样本中有13个相对误差在20%以内,针对有限样本的数据分析中更为适用。结论该方法有效提高了大气腐蚀环境数据缺失的填补精度,对在数据缺失情况下预测大气腐蚀速率具有重要意义。     

Modeling of residential lighting load profile using adaptive neuro fuzzy inference system (ANFIS)

Most practices currently adopted in modeling the presence of people in residential houses do not reflect the complexity of the impact occupants have on lighting loads. Hence the needs to contribute a methodology, that will be able to look into such characteristics. This study entailed the use of adaptive Neuro Fuzzy inference system (ANFIS) for estimation and prediction of lighting load usage profile for energy and demand side management initiatives. Results obtained showed a better correlation analysis and root mean square error (RMSE) in contrast to the regression method model. This shows that ANFIS model has good prediction accuracy capability.     

A conceptual framework for Jatropha seed yield estimation using adaptive neuro-fuzzy inference system (ANFIS) modelling

Jatropha seed yield prediction is one of the most important influencing factors for developing a supply chain modelling of Jatropha seed. The oil from this Jatropha seed is used to blend with diesel to obtain biofuel (Hiromi, Yamamoto, Junichi Fujino, and Kenji Yamaji. 2001. “Evaluation of Bioenergy Potential with a Multi-regional Global-Land-Use-and-Energy Model.” Biomass and Bioenergy 21: 185–203). The Jatropha plant is easy to cultivate and produces high yield if properly maintained. The main focus of this study is to utilize uncultivable wastelands for cultivating Jatropha seeds. As suggested by Liu et al. (Liu, Xiaohong, David B. Grant, Alan C. McKinnon, and Yuanhua Feng. 2010. “An Empirical Examination of the Contribution of Capabilities to the Competitiveness of Logistics Service Providers: A Perspective from China.” International Journal of Physical Distribution & Logistics Management 40 (10): 847–866), the effectiveness of prediction affects the functional characteristics of a supply chain network design. The yield prediction of Jatropha seeds has two important roles which include (i) the identification of external parameters that affects the yield and (ii) the detection of internal attributes that changes the growth characteristics of the Jatropha plant. The development of the fuzzy inference system is characterized by a large number of input variables (Dobrila Petrovic. 2001. International Journal of Production Economics 71: 429–438). A Matlab programming software was used to integrate an adaptive neuro-fuzzy inference system. This approach gave the numerical as well as graphical output that was used to interpret the final result. The root mean square error values were identified for the given inputs which were then compared with the trained input variables to select the best input among the given alternative variables.     

位移反分析的PSA-ANFIS方法研究

地下工程中,通常采用位移反分析方法来确定围岩的力学参数,而现有的位移反分析方法均存在诸多不足之处,为此,采用模式搜索算法、自适应神经模糊推理系统以及正交试验设计方法,建立了位移反分析的PSA-ANFIS方法;同时还对一标准弹塑性问题进行了解析计算,得到了相应的训练数据对、检测数据对和预测数据对;最后采用此方法对该弹塑性问题的力学参数进行了反演。反演结果表明,所建立的位移反分析的PSA-ANFIS方法除了能够充分发挥ANFIS的各种优点外,还能有效地减少模型建立和参数调节过程中的人为干预,因此是一种优异的位移反分析方法。