Robotics and Automation in the Food Industry

Current and Future Technologies

Darwin G Caldwell

• Publisher: Woodhead Publishing
• Year: 2012
• Print ISBN:
  9781845698010
• eBook ISBN:
  9780857095763
• Topic: Technology & Engineering
• Subtopic: Food Science
• Index: Technology & Engineering

The implementation of robotics and automation in the food sector offers great potential for improved safety, quality and profitability by optimising process monitoring and control. Robotics and automation in the food industry provides a comprehensive overview of current and emerging technologies and their applications in different industry sectors.Part one introduces key technologies and significant areas of development, including automatic process control and robotics in the food industry, sensors for automated quality and safety control, and the development of machine vision systems. Optical sensors and online spectroscopy, gripper technologies, wireless sensor networks (WSN) and supervisory control and data acquisition (SCADA) systems are discussed, with consideration of intelligent quality control systems based on fuzzy logic. Part two goes on to investigate robotics and automation in particular unit operations and industry sectors. The automation of bulk sorting and control of food chilling and freezing is considered, followed by chapters on the use of robotics and automation in the processing and packaging of meat, seafood, fresh produce and confectionery. Automatic control of batch thermal processing of canned foods is explored, before a final discussion on automation for a sustainable food industry.With its distinguished editor and international team of expert contributors, Robotics and automation in the food industry is an indispensable guide for engineering professionals in the food industry, and a key introduction for professionals and academics interested in food production, robotics and automation. - Provides a comprehensive overview of current and emerging robotics and automation technologies and their applications in different industry sectors - Chapters in part one cover key technologies and significant areas of development, including automatic process control and robotics in the food industry and sensors for automated quality and safety control - Part two investigates robotics and automation in particular unit operations and industry sectors, including the automation of bulk sorting and the use of robotics and automation in the processing and packaging of meat, seafood, fresh produce and confectionery

Part I

Introduction, key technologies and significant areas of development

1

Automatic process control for the food industry: an introduction

Y. Huang¹, United States Department of Agriculture, USA

Abstract:

In order to ensure food security in food-manufacturing operations, automatic process control is desired. With the operation of automatic process-control systems the deviation of the controlled variables from standards can be consistently monitored, adjusted, and minimized to improve the process operations on a regular basis. Proportionalintegral-derivative (PID) control has been widely used in the food industry. Model-based control has been developed to improve the performance of control systems in the food industry. This chapter overviews the concepts, methods, and systems of automatic process control for the food industry, and projects the future of automatic process control.

Key words

food industry

automation

process control

PID

model-based control

1.1 Introduction

The food industry includes collective businesses and manufacturers that together supply food products for people to consume. Food security in quality and safety are the primary concerns for the food industry. In order to assure the quality and safety of food security, process control is needed to improve food-manufacturing operations. Process control is realized by using the difference between the measured values of the controlled variable(s) and their desired values to regulate the process output to meet performance requirements. Process control can be implemented manually based on the judgment of human operators. Although highly trained human operators are intelligent and able to perceive the deviation of the controlled variable(s) from the standards when problems occur, their judgments may not be consistent due to fatigue or other unavoidable mental and physical stresses, which may result in inconsistency of food products. With the development of control theory, electronic technology, and computer engineering, automatic process control becomes possible. With the operation of the automatic process-control systems, deviation of the controlled variable(s) from the standards can be consistently monitored, and the difference between the measured and desired values can be consistently used to adjust and improve process operations on a regular basis.

In general, automatic process control (called process control hereafter) goes through a procedure as follows:

1. Specify desired values of the controlled variable(s).

2. Measure samples for actual values of the controlled variable(s).

3. Calculate the difference between the actual and desired values of the controlled variable(s).

4. Input the difference to operate the pre-designed controller to adjust the controlled variable(s) to reduce the difference.

Design of a controller is important for successful process control. PID control has been widely used in the food industry. To improve the performance of the control systems, model-based control has been developed (Haley and Mulvaney, 1995). This article will overview the concepts, methods, and systems of process control for the food industry, and project the future of process control in that industry.

1.2 Process control systems and structure in the food industry

In a process control system, computers are key to driving and managing the operation of the system. A computer for process control includes hardware, such as the computer itself, peripherals, instrumentation, input–output equipment, and system and application software. Process control engineers, food scientists, and engineers are responsible for developing the application software-based control algorithms.

Two types of process control systems exist: open-loop and closed-loop. In open-loop systems, the system output is controlled directly only by the input signal, with the output having no effect on the system input. The system cannot compensate for any unexpected conditions in the system output. Closed-loop systems monitor the system output, and feed the output measurement back to the control computer, which continuously minimizes the difference between the measured output and desired output, by adjusting the controller input. Feedback on how the system is actually performing allows the controller to dynamically compensate for disturbances to the system. Compared to closed-loop control systems, open-loop control systems are less commonly used because they are less accurate. With the benefit of feedback, the closed-loop control systems have been widely used in the food industry. The measurement of the system output from food-manufacturing operations is fed back to be compared to the desired value(s) of the variable and to adjust the system output to minimize the error between the measured and desired outputs.

1.3 Process control methods in the food industry

In the food industry, various control schemes have been designed and used (Haley and Mulvaney, 1995). Conventional PID controllers have been applied to different processes. However, PID controllers cannot work well consistently, because they are mostly suitable for processes with low-order linear dynamics. In order to improve the performance of process control systems for processes that have high-order non-linear dynamics with time-variant parameters, more advanced control schemes have been designed and applied.

Model-based controllers are designed based on the prediction of process models (analytical and empirical) and process control requirements. Artificial neural networks (ANNs) and fuzzy logic (FL) are two of the most common approaches to establishing the relationship between the input and output of the system to estimate the process output instead of measuring it directly. Neuro-fuzzy controllers take the advantages of ANNs and FL to establish the relationship between the input and output of the system to infer the process output that cannot be measured directly. FL and ANNs are two soft computing techniques (Huang et al., 2010). They have been used in food science and technology (Eerikainen et al., 1993). Other soft computing techniques, such as genetic algorithms (GAs) and support vector machines (SVMs), also have the potential to be used in designing process controllers.

1.3.1 Proportional-integral-derivative controller

PID controllers have been developed for the classic closed-loop feedback control scheme (Ang et al., 2005). This development is based on the study of the dynamics of second-order linear systems. In order to direct the system to perform as desired a PID controller can be added at the input to form a feedback closed-loop (Fig. 1.1). A PID controller can be expressed by the following equation:

Fig. 1.1 A feedback closed-loop control scheme with PID controller.

[1.1]

where u(t) is the output of the PID controller and the input to the process at time instant t, e(t) = y^s(t) – y(t) is the difference between the system output y(t) and the

desired value y^s(t) at time instant t, K_p is the proportional gain constant, K_i is the integral gain constant, and K_d is the derivative gain constant.

Figure 1.2shows the definition of controller tuning parameters in response to a step input. The proportional control mode simply has the effect of reducing the rise time, and reduces but never eliminates the steady-state error....

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributor contact details
• Woodhead Publishing Series in Food Science, Technology and Nutrition
• Part I: Introduction, key technologies and significant areas of development
• Part II: Robotics and automation in particular unit operations and industry sectors
• Index