Purge and pressurization is an alternative hazardous location protection concept that allows lesser rated equipment to be used in hazardous areas by segregating the equipment from the hazardous material.
Process measurements are instantaneous but analyzer responses never are. From the tap to the analyzer, there is always a time delay. Unfortunately, this delay is often underestimated or misunderstood.
Time delay is defined as the amount of time it takes for a new sample to reach the analyzer. One way to control time delay is with a regulator. Regulators control pressure, and pressure in an analytical system is closely related to time. In the case of gas systems with a controlled flow rate, the lower the pressure, the shorter the time delay.
Delay may occur in any of the major parts of an analytical instrumentation (AI) system, including the process line, tap and probe, field station, transport line, sample conditioning system, stream switching system, and analyzer.
When you have hundreds of different data acquisition (DAQ) devices to choose from on a wide variety of buses, it can be difficult to select the right bus for your application needs. Each bus has different advantages and is optimized for throughput, latency, portability or distance from a host. This white paper examines the most common PC bus options and outlines the technical considerations to keep in mind when choosing the right bus for your measurement application.
Process measurements are instantaneous, but analyzer responses never are. From the tap to the analyzer, there is always a delay. Unfortunately, this time delay is often underestimated or not accounted for or understood. Time delay in sample systems is the most common cause of inappropriate results from process analyzers.
In many cases, it is invisible to operators and technicians, who are focused on the necessity of making the sample suitable for the analyzer. It is not unusual for operators to assume that the analytical measurement is instantaneous. In fact, sample systems often fail to achieve the industry standard of a one minute response.
As a general rule, it's always best to minimize time delay, even for long cycle times, but delays extending beyond the industry standard are not necessarily a problem. The process engineer determines acceptable delay times based on process dynamics.
Delays become an issue when they exceed a system designer's expectations. A poor estimate or wrong assumption about time delay will necessarily result in inferior process control.
This article is intended to enhance understanding of the causes of time delay and to provide the tools required to calculate or approximate a delay within a reasonable margin of error. We will also provide some recommendations for reducing time delay. The potential for delay exists in the follow sections of an analytical instrumentation (AI) system: process line, tap and probe, field station, transport line, sample conditioning system, stream switching system, and analyzer.
In many analytical instrumentation systems, the analyzer does not provide an absolute measurement. Rather, it provides a relative response based on settings established during calibration, which is a critical process subject to significant error. To calibrate an analyzer, a calibration fluid of known contents and quantities is passed through the analyzer, producing measurements of component concentration. If these measurements are not consistent with the known quantities in the calibration fluid, the analyzer is adjusted accordingly. Later, when process samples are analyzed, the accuracy of the analyzer's reading will depend on the accuracy of the calibration process. It is therefore, imperative, that we understand how error or contamination can be introduced through calibration; when calibration can - and cannot - address a perceived performance issue with the analyzer; how atmospheric pressure or temperature fluctuations can undo the work of calibration; and when and when not to calibrate.
The objective of an analytical instrumentation (AI) system is to provide a timely analytical result that is representative of the fluid in the process line at the time the sample was taken. If the AI system alters the sample so the analytical result is changed from what it would have been, then the sample is no longer representative and the outcome is no longer meaningful or useful. Assuming the sample is properly taken at the tap, it may still become unrepresentative under any of the following conditions:
- If deadlegs or dead spaces are introduced at inappropriate locations in the AI system, resulting in a "static leak," a bleeding or leaking of the old sample into the new sample;If the sample is altered through contamination, permeation, or adsorption;
- If the balance of chemicals is upset due to a partial change in phase; or
- If the sample undergoes a chemical reaction.
This article will review the major issues leading to an unrepresentative sample and provide recommendations on how to avoid a compromised sample. It will discuss deadlegs and dead spaces; component design and placement; adsorption and permeation; internal and external leaks; cross contamination in stream selection; and phase preservation.
Events over the last several years have focused attention on certain types of loads on the electrical system that result in power quality problems for the user and utility alike. Equipment which has become common place in most facilities including computer power supplies, solid state lighting ballast, adjustable speed drives (ASDs), and un-interruptible power supplies (UPSs) are examples of non-linear loads. Adjustable speed drives are also known as Variable Frequency Drives (VFDs) and are used extensively in the HVAC systems and in numerous industrial applications to control the speed and torque of electric motors. The number of VFDs and their power rating has increased significantly in the past decade. Hence, their contribution to the total electrical load of a power system is significant and cannot be neglected.
Non-linear loads are loads in which the current waveform does not have a linear relationship with the voltage waveform. In other words, if the input voltage to the load is sinusoidal and the current is non-sinusoidal then such loads will be classified as non-linear loads because of the non-linear relationship between voltage and current. Non-linear loads generate voltage and current harmonics, which can have adverse effects on equipment that are used to deliver electrical energy. Examples of power delivery equipment include power system transformers, feeders, circuit breakers, etc. Power delivery equipment is subject to higher heating losses due to harmonic currents consumed by non-linear loads. Harmonics can have a detrimental effect on emergency or standby power generators, telephones and other sensitive electrical equipment.
When reactive power compensation in the form of passive power factor improving capacitors are used with non-linear loads, resonance conditions can occur that may result in even higher levels of harmonic voltage and current distortion thereby causing equipment failure, disruption of power service, and fire hazards in extreme conditions.
The electrical environment has absorbed most of these problems in the past. However, the problem has now reached a magnitude where Europe, the US, and other countries have proposed standards to engineer systems responsibly, considering the electrical environment. IEEE 519-1992 and EN61000-3-2 have evolved to become a common requirement cited when specifying equipment on newly engineered projects. Various harmonic filtering techniques have been developed to meet these specifications. The present IEEE 519-1992 document establishes acceptable levels of harmonics (voltage and current) that can be introduced into the incoming feeders by commercial and industrial users. Where there may have been little cooperation previously from manufacturers to meet such specifications, the adoption of IEEE 519-1992 and other similar world standards now attract the attention of everyone.
Variable Frequency Drives (VFDs) with diode rectifier front end are typically equipped with a resistorcontactor arrangement to limit the inrush current into the dc bus capacitors, thereby providing a means for soft charging the dc bus capacitors. Because of the mechanical nature of the magnetic contactor typically used in VFDs, there exists a concern for fatigue. In addition, during a brown out condition, typically the contactor remains closed and when the voltage recovers, the ensuing transient is often large enough to possibly cause unfavorable influence to surrounding components in the VFD. Many researchers and application engineers have thought about this issue and many are actively seeking non-mechanical solutions in a cost effective manner.
In this paper, a new topology to soft charge the dc bus capacitor is proposed. Other techniques that have been evaluated are also introduced. The relative advantages and disadvantages are discussed. Experimental tests to show the feasibility of the proposed idea is also provided.
Mahesh Swamy, Tsuneo J. Kume and Noriyuki Takada, Yaskawa Electric America
Diode rectifier with large DC bus capacitors, used in the front ends of Variable Frequency Drives (VFDs), draw discontinuous current from the power system resulting in current distortion and hence voltage distortion. Typically, the power system can handle current distortion without showing signs of voltage distortion. However, when the majority of the load on a distribution feeder is made up of VFDs, current distortion becomes an important issue. Multi-pulse techniques to reduce input harmonics are popular because they do not interfere with the existing power system either from higher conducted EMI when active techniques are used or from possible resonance, when capacitor based filters are employed.
In this paper, a new 18-pulse topology is proposed that has two six-pulse rectifiers powered via a phase-shifting isolation transformer, while the third six-pulse rectifier is fed directly from the AC source via a matching-impedance. This idea relies on harmonic current cancellation strategy rather than the flux cancellation method and results in lower overall harmonics. It is also seen to be smaller in size and weight, and lower in cost compared to an isolation transformer. Experimental results are given to validate the concept.
Mahesh Swamy, Tsuneo J. Kume and Noriyuki Takada, Yaskawa Electric America
Customers in all industries are coming more and more under pressure to measure the cost of their utilities. Important drivers for this pressure are the rising cost of energy and various certifications according to EMAS and the ISO 14000 series. Measuring utilities has been neglected in the past and using calibrated technology is necessary for this process. However, many companies only measure their utility consumption at the custody transfer point, and these few measuring occurrences leave room for inaccuracy and poor energy management. By investing money in efficient measuring tools, is possible to set up energy monitoring systems that measure the consumption of each respective utility close to the point of use. This white paper reviews processes that can help you attain better energy management. Download now to learn more.
Process Analytics and Intelligencesometimes called Manufacturing Intelligencehas transformed the way companies produce goods, understand their manufacturing processes, and ensure a quality product in ways we could not have foreseen ten years ago.
Real-time Analytics have replaced the legacy concept of running reports. Reports that represent a static picture of a process at a fixed point in time are great tools for compliance audits and long term warranty analysis. However, they may not accurately represent the "as-is" state of a process. Reports showing large amounts of data can be difficult to interpret. There are often limitations in how the report data can be drilleddown and viewed.
With today's large volumes of data, there's a wealth of information that can be gained about the process. But how can this data be captured, managed and retrieved in a way that presents the information in an up-to-theminute easy to understand format? Real-time Analytics provides the techniques and solutions that address this problem. Instead of users having to interpret the data, it's presented in a graphical form enabling them to easily drill down to explore the data in real-time.
This white paper discusses how Process Analytics is implemented and utilized. Ways of managing and distributing Process Analytics to the organization are presented.
Download this resource kit to learn more about green engineering and sustainable design practices that are being developed and deployed with products and technologies. This kit contains three webcasts, a tutorial on fuel-cell testing and a case study featuring Data Science Automations development of a reliable mercury emissions sampling system.
Applicator Selection is a product selection guide for the product lines of Level, Flow, Density, Pressure, Analysis, Temperature, Data Acquisition and System Components. Applicator suggests suitable products and their components and displays product images and properties.
With Alarm Manager, Kinder Morgan Canada will be able to identify bad actors and ensure all alarms are configured in a way that maximizes safety and efficiency. By reducing alarms, the solution helps operators notice the most important ones, thereby decreasing operator error.
The purpose of this paper is to present test results of recent measurements on a scintillating fill fluid (i.e., liquid scintillator filled) detector and a scintillating fiber bundle detector designs, and explain the observed differences in efficiency. In our measurements we observe an improvement of factor of 2.4 in light output for a fill fluid detector compared to scintillating fiber bundle detector of the same diameter and length.
This white paper explores fuzzy logic and how it helps engineers solve nonlinear control problems commonly found in process applications. Fuzzy logic, which mathematically emulates human reasoning, provides an intuitive way to design function blocks for intelligent control systems, advanced fault detection and other complex applications. Control systems deploying fuzzy logic can improve the management of uncertain variables, such as temperature fluctuations.
This paper discusses the scope and approach taken to integrate quality information from various analytical devices through a Laboratory Information Management System (LIMS) with a real-time process information system information connected to a distributed control system (DCS).