Reducing Refrigeration Costs in the Dairy Plant

University — industry cooperative study on plant operations

The prospect of a fully-automated technology has been the subject of considerable discussion in the literature of the sociology of work. Many writers have viewed automation as reversing the trends towards extreme fragmentation of tasks and authoritarian management characteristic of mass production technology. Workers will no longer perform simple tasks; they will become responsible for the operation of entire plants. The earlier work of Blauner (1964), who saw automation bringing a new age of ‘responsible workers’ in which operators of plants would enjoy ‘consultation’ with management has influenced this view. Duncan Gallie, for example, believes that automation has ‘blunted the clear-cut distinction between manual and non-manual labour’ and he emphasises ‘the significance of the operator’s work performance for the quantity, quality and cost of the product’ (1978, p. 214). The notion that automation is only effective if workers are given responsibility is the underlying premise of a study by Larry Hirschhorn. ‘In cybernetic settings workers must control the controls. To do so they cannot merely become competent at a fixed and predictable set of tasks … Instead, they must be able to survey and understand the entire production process’ (1986, p. 2). The complexity and flexibility of automated production, as well as the need to attend to the efficiency, reliability and safety of production, requires workers who are knowledgeable, exercise judgement and maintain a consultative relationship with management.

A plant operator performance evaluation system to analyze plant operation records during accident training and to identify and classify operator errors has been developed for the purpose of supporting realization of a training and education system for plant operators. A knowledge engineering technique was applied to evaluation of operator behavior by both event-based and symptom-based procedures, in various situations including event transition due to multiple failures or operational errors The system classifies the identified errors as to their single and double types based on Swain's error classification and the error levels reflecting Rasmussen's cognitive level, and it also evaluates the effect of errors on plant state and then classifies error influence, using “knowledge for phenomena and operations”, as represented by frames. It has additional functions for analysis of error statistics and knowledge acquisition support of “knowledge for operations”. The system was applied to a training analysis for a scram event in a BWR plant, and its error analysis function was confirmed to be effective by operational experts.

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Parametric investigation of a renewable alternative for utilities adopting the co-firing lignite/biomass concept

Despite the high degree of automation in industrial control systems, human operators in industrial plants play a critical role in ensuring uptime, production quality, and safety. Plant operators do so by not only monitoring the process but also intervening when the process runs into unusual or exception situations. In this paper, we present to ensure smooth plant operation by automatically identifying and investigating potential upcoming issues as well as providing recommendations to plant operators on how to address them with confidence. This is achieved by applying Artificial Intelligence (AI) techniques including deep learning, process mining, and graph search, on historical industrial process data such as alarm and event data, audit trails, engineering documents, and safety procedures. Our solution has been validated on data from the Draugen oil field operated by the Norwegian oil and gas company, OKEA.

Human characteristics of plant operation and man-machine interface

Real-time Simulation (RTS) has long been used in the nuclear power industry for operator training and engineering purposes. And, online simulation (OLS) is based on RTS and with connection to the plant information system to acquire the measurement data in real time for calibrating the simulation models and following plant operation, for the purpose of analyzing plant events and providing indicative signs of malfunctioning. OLS has been applied in certain industries to improve safety and efficiency. However, it is new to the nuclear power industry. A research project was initiated to implement OLS to assist operators in certain critical nuclear power plant (NPP) operations to avoid faulty conditions. OLS models were developed to simulate the reactor core physics and reactor/steam generator thermal hydraulics in real time, with boundary conditions acquired from plant information system, synchronized in real time. The OLS models then were running in parallel with recorded plant events to validate the models, and the results are presented.

In recent decades, the aging of nuclear power plants, the upgrading of safety systems and the concern for life extension of licensed plants close to completing 40 years of operation have been considered by regulatory agencies. The extension of operating licenses for power reactors over 40 years has been a viable option for operators of nuclear power plants to ensure the adequacy of future capacity of power generation, in terms of economic benefits, compared with the construction of new power reactors. Routine reviews of nuclear power plant operation (including modifications to hardware and procedures, significant events, operating experience, plant management and personnel competence) and special reviews following major events of safety significance are the primary means of safety verifications. Rereviews include an assessment of plant design and operation against current safety standards and practices, and they aim at ensuring a high level of safety throughout the plant's operating lifetime. They are complementary to the routine and special safety reviews and do not replace them (IAEA, 2009). In 1991, the US Nuclear Regulatory Commission (NRC) issued rules and associated documentation describing how the licensee must demonstrate that the unit can continue operating for 20 years following the expiration of the 40-year license. These rules were established in 10CFR51 (NRC, 2007a), environmental protection requirements, and 10CFR54 (NRC, 2010a), technical requirements. In 1994, the International Atomic Energy Agency (IAEA) issued recommendations, guidance and associated documentation describing how the licensee must demonstrate that the unit can continue operating through a systematic safety reassessment, named periodic safety review (PSR) (IAEA, 1994(IAEA, , 2003)). The safety guide has the purpose of providing recommendations and guidance on the conduct of a PSR each 10 years for an existing nuclear power plant, and it is directed at plant operating organizations and regulators. In addition, some member states have initiated this reassessment to evaluate the cumulative effects of plant aging and plant modifications, operating experience, technical developments and siting aspects.

Due to guaranteed feed-in tariffs under the Renewable Energy Act and the feed-in precedence of renewable power generation plants the operation of biogas plants in Germany is currently plannable and economically advantageous. However, it is foreseeable that without this subvention biogas plants cannot compete with other regenerative plants such as photovoltaic and wind power plants on the open electricity market. Accordingly, it is of great importance for biogas plant operators to identify and occupy suitable niches to make full use of the unique features of their plants. Because of their predictable availability, those plants can particularly benefit of earning opportunities in times of high demand and contribute to grid stabilization. In order to keep the effort for plant operators as low as possible the automation of existing biogas plants can be extended and enabled to communicate with market platforms or control centers of the distribution system operator. Thus biogas plants can contribute to balancing group compensation not only for accounting purposes but factual by appropriate feed-in into the electrical network in consideration of actual demand.

Pacific Northwest National Laboratory personnel have developed a cost-effective solution for implementing the use of advanced technologies for monitoring the condition and performance of aging industrial facilities. A combination of operations and maintenance (O & M) know-how together with Laboratory technical capabilities have been used to develop and demonstrate the effectiveness of a condition monitoring software system. Already proven in a moderate size pilot heating plant, the system is expected to pay large dividends in the reduction of O & M costs in an aging cogeneration facility. Additional projects are currently underway to develop this technology to its full potential. This advanced architecture was designed to provide each segment of the plant operations and maintenance (O & M) team with understandable information for making safe, cost-effective life-cycle operating decisions. The software will provide plant operators, maintenance technicians, engineering staff and administrators with on-target, on-line information that enables high process efficiency simultaneously with cost- effective, life cycle oriented, capital equipment management. This infrastructure information becomes increasingly critical as the equipment, systems, and the facility itself become older. The result of this research provides the O & M practitioner with the ability to intelligently select the asset management course of action that minimizes both the cost and risk engendered by the operation and maintenance of aging process facilities.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Cost allocation evaluation of a multi-plant flexible heat exchanger network design based on fuzzy game

Geothermal systems are frequently used as a source of low-emissions energy. However, reservoirs with high amounts of dissolved CO2 can produce substantial power plant emissions, exceeding 100 kt/yr in some cases. Accurately accounting for the net anthropogenic emissions at geothermal systems is difficult because these systems also naturally flux CO2, sometimes at a magnitude similar to plant emissions, and this natural flux may increase or decrease due to plant operations. Furthermore, recent efforts in Iceland and New Zealand to capture and reinject geothermal emissions can further alter CO2 fluxes and raise the amount of CO2 stored in the subsurface.In the absence of direct monitoring data, mathematical models can be used to estimate geothermal CO2 emissions. Here, I describe a lumped parameter model of CO2 flux through a liquid geothermal reservoir. The model parameterises CO2 influx due to magmatic degassing, CO2 loss from vertical migration through caprock or lateral outflow, degassing due to pressure or boiling driven solubility changes, CO2 extraction and reinjection through wells, and permanent storage through mineralisation reactions. Under constant mass extraction and suitable simplifying assumptions, the model can be solved exactly yielding exponential approximations of emissions rates (natural and plant), and reservoir CO2 content.Calibration of this model to ten years of plant emissions and pressure decline data at Rotokawa and Ngā Tamariki geothermal fields (New Zealand), suggest that degassing trends are largely driven by CO2 dilution of the liquid reservoir. Furthermore, both depressurisation and dilution substantially lower the natural CO2 outflow, an effect not presently accounted for in greenhouse gas inventories. This means that measured plant emissions may exceed the true anthropogenic impact on geothermal emissions by up to a factor of three, which has substantial financial implications for geothermal plant operators.

Industrial plan alarm systems form an essential part of the operator interfaces for automatically monitoring plant state deviations and for attracting plant operators' attention to changes that require their intervention. To design effective plant alarm systems, it is essential to evaluate their performances. In this paper, some performance monitoring methods of plant alarms systems for alarm system rationalization are reviewed.

The Theory of Constraints was used as a method in a root cause analysis (RCA) to identify system performance bottlenecks, wherein the constraint with the strongest correlation with system performance is identified as the bottleneck. In the present study, this method was applied in a large district cooling plant to diagnose steam absorption chillers’ performance anomalies including performance deterioration, spikes, large variation, etc. Many root causes were identified through a ten months’ continuous diagnosis, such as drastic reduction in chilled water flow rate, faulty steam control valve, cooling demand reduction, etc. More than 30 anomalies were diagnosed and more than 80% diagnosis accuracy was achieved. The diagnosis results were verified by the plant operator, which proved the effectiveness of the Theory of Constraints as a method for root cause analysis in diagnosing anomalies in district cooling plant operations. It was however discovered that even though the operational component identification accuracy was high, the determination of the actual root cause still presents lots of challenges, due to the large set of domain knowledge and operation conditions required. A knowledge-based supporting tool may be needed as part of the RCA to facilitate a speedy and complete analysis to enable these anomalies to be resolved.