A framework for the successful implementation of food traceability systems in China

Implementation of food traceability systems in China faces many challenges due to the scale, diversity and complexity of China’s food supply chains. This study aims to identify critical success factors specific to the implementation of traceability systems in China. Twenty-seven critical success factors were identified in the literature. Interviews with managers at four food enterprises in a pre-study helped identify success criteria and five additional critical success factors. These critical success factors were tested through a survey of managers in eighty-three food companies. This study identifies six dimensions for critical success factors: laws, regulations and standards; government support; consumer knowledge and support; effective management and communication; top management and vendor support; and information and system quality

Critical success factors for implementing traceability systems in Chinese food enterprises

A thesis submitted for the degree of Master of Science by research of the University of BedfordshireFood safety has always been the focus of worldwide attention. Chinese government has promulgated a series of initiatives, laws and regulations to implement the traceability systems since 2004. However, the implementation of traceability system (TS) in China faces many challenges which are creating major barriers to the traceability system implementation success. This research aims to identify critical success factors (CSF) for implementing TS in Chinese food enterprises. More specifically, the study attempts to develop a set of criteria of TS implementation success from theoretical and practical point of views and identify and propose a framework of critical success factors for TS implementation success.To achieve the research objectives, this research adopted both qualitative and quantitative approaches. Extensive literature review was conducted to establish initial understanding of TS implementation success and associated critical success factors. Semi-structured interviews were carried out with six managers to establish a set of TS implementation success measures in the context of Chinese food enterprises. Survey questionnaires were designed to identify the critical success factors influencing TS implementation success. Primary data were collected from 124 valid responses in China. Descriptive and factor analysis were conducted using SPSS. According to the survey, the top five critical success factors are: the authenticity of traceability information; perfect food traceability laws; perfect food traceability standards; clear objectives for traceability system implementation; policy guidance for enterprises traceability system implementation from government. The framework has six dimensions of critical success factors including: laws, regulations & standards; government support & guidance; consumer knowledge & support; top management, company-wide & vendor support; efficient management & communication; information quality & system quality. Based on the exploratory factor analysis a CSF framework for implementing TS in Chinese food enterprises was proposed. The outcomes of this research will have great significance for research and management in implementing TS and offering implications for policy makers and other stakeholders in the future

Transient Controller Design Based on Reinforcement Learning for a Turbofan Engine with Actuator Dynamics

To solve the problem of transient control design with uncertainties and degradation in the life cycle, a design method for a turbofan engine’s transient controller based on reinforcement learning is proposed. The method adopts an actor–critic framework and deep deterministic policy gradient (DDPG) algorithm with the ability to train an agent with continuous action policy for the continuous and violent turbofan engine state change. Combined with a symmetrical acceleration and deceleration transient control plan, a reward function with the aim of servo tracking is proposed. Simulations under different conditions were carried out with a controller designed via the proposed method. The simulation results show that during the acceleration process of the engine from idle to an intermediate state, the controlled variables have no overshoot, and the settling time does not exceed 3.8 s. During the deceleration process of the engine from an intermediate state to idle, the corrected speed of high-pressure rotor has no overshoot, the corrected-speed overshoot of the low-pressure rotor does not exceed 1.5%, and the settling time does not exceed 3.3 s. A system with the designed transient controller can maintain the performance when uncertainties and degradation are considered

Transient Controller Design Based on Reinforcement Learning for a Turbofan Engine with Actuator Dynamics

To solve the problem of transient control design with uncertainties and degradation in the life cycle, a design method for a turbofan engine’s transient controller based on reinforcement learning is proposed. The method adopts an actor–critic framework and deep deterministic policy gradient (DDPG) algorithm with the ability to train an agent with continuous action policy for the continuous and violent turbofan engine state change. Combined with a symmetrical acceleration and deceleration transient control plan, a reward function with the aim of servo tracking is proposed. Simulations under different conditions were carried out with a controller designed via the proposed method. The simulation results show that during the acceleration process of the engine from idle to an intermediate state, the controlled variables have no overshoot, and the settling time does not exceed 3.8 s. During the deceleration process of the engine from an intermediate state to idle, the corrected speed of high-pressure rotor has no overshoot, the corrected-speed overshoot of the low-pressure rotor does not exceed 1.5%, and the settling time does not exceed 3.3 s. A system with the designed transient controller can maintain the performance when uncertainties and degradation are considered

A Multi-Cavity Iterative Modeling Method for the Exhaust Systems of Altitude Ground Test Facilities

To solve the modeling problem of altitude ground test facility (AGTF) exhaust systems, which is caused by nonlinearity along the gas path and the difficulty of ejection factor calculation, a multi-cavity iterative modeling method is presented. The components of exhaust systems, such as the exhaust diffuser and cooler, are built with a series of volumes. It overcomes the disadvantage that traditional lumped-parameter models have, whereby they cannot calculate the dynamic parameters along the gas path. The exhaust system model is built with an iterative method based on multi-cavity components, and simulations are carried out under experimental conditions. The simulation results show that the maximum error of pressure is 2 kPa in the steady state and less than 6 kPa in the transient process compared with experimental data. Closed-loop simulations are also carried out to further verify the accuracy and effectiveness of the multi-cavity iterative exhaust system modeling method

MoHydroLib: An HMU Library for Gas Turbine Control System with Modelica

Modelica is an open-source, object-oriented equation-based modeling language. It is suitable for describing sophisticated dynamic systems (symmetry/asymmetry) as it uses mathematical acausal equations to express physical characteristics. The hydraulic mechanical units (HMU) of gas turbine engine control systems couple the contents of mechanical, hydraulic, symmetry, and other multidisciplinary fields. This paper focuses on the Modelica description method of those HMU models. The content of this work is threefold: firstly, the division form of basic elements in HMU is defined, and the method for describing these element models with Modelica is proposed; secondly, the organization of the element models is defined by using the inheritance characteristics of Modelica, and a lightweight (small code scale) component model is designed; and finally, the causal/acausal connections are designed according to bond graph theory, and the elements and components are integrated into a prototype modeling library. In this paper, the modeling library is verified by comparing simulation results of five typical HMU subsystem models with commercial modeling and simulation software

MoHydroLib: An HMU Library for Gas Turbine Control System with Modelica

Modelica is an open-source, object-oriented equation-based modeling language. It is suitable for describing sophisticated dynamic systems (symmetry/asymmetry) as it uses mathematical acausal equations to express physical characteristics. The hydraulic mechanical units (HMU) of gas turbine engine control systems couple the contents of mechanical, hydraulic, symmetry, and other multidisciplinary fields. This paper focuses on the Modelica description method of those HMU models. The content of this work is threefold: firstly, the division form of basic elements in HMU is defined, and the method for describing these element models with Modelica is proposed; secondly, the organization of the element models is defined by using the inheritance characteristics of Modelica, and a lightweight (small code scale) component model is designed; and finally, the causal/acausal connections are designed according to bond graph theory, and the elements and components are integrated into a prototype modeling library. In this paper, the modeling library is verified by comparing simulation results of five typical HMU subsystem models with commercial modeling and simulation software