A STRUCTURED FRAMEWORK FOR RELIABILITY AND RISK EVALUATION IN THE MILK PROCESS INDUSTRY UNDER FUZZY ENVIRONMENT

This paper aims at proposing a novel integrated framework for studying reliability and risk issues of the curd unit in a milk process industry under uncertain environment. The considered plant’s complex series-parallel configuration was presented using the Petri Net (PN) modeling. The Fuzzy Lambda-Tau (λ-τ) approach was applied to study and analyze the reliability aspects of the considered plant. Failure dynamics of the curd unit has been analyzed with respect to increasing/ decreasing trends of the tabulated reliability indices. Availability of the considered plant shows a decreasing trend with an increase in spread values. For improving the system’s availability, a risk analysis was done to identify the most critical failure causes. Using the traditional FMEA approach, the FMEA sheet was generated on the basis of expert’s knowledge/experience. The Fuzzy-Complex Proportional Assessment (FCOPRAS) approach was applied within FMEA approach for identification of critical failure causes associated with different subsystem/components of the considered plant. In order to check the consistency of the ranking results, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) was applied within the FCOPRAS approach. Ranking results are compared for checking consistency and robustness of critical failure causes related decision making which would be useful in designing the finest maintenance schedule for the considered curd unit.  Overheating/moisture lead to winding failure (MSCP5), visible sediment of milk jam in filter (MBFP3), improper quality of oil (H4), blade breakage (CTK4), wearing in gears (PFM11), and cylinder leakage (CFM7) were recognized as the most critical failure causes contributing to system unavailability. The analysis results were supplied to the maintenance manager for framing a suitable time-based maintenance intervals policy for the considered unit

Simulation and Economic Analysis of Coal Based Thermal Power Plant: A Critical Literature Review

ABSTRACT: Coal based fired power plant is a very complex unit. Today's electric energy is playing an important role in the industria

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Advances in Postharvest Process Systems

This Special Issue presents a range of recent technologies and innovations to help the agricultural and food industry to manage and minimize postharvest losses, enhance reliability and sustainability, and generate high-quality products that are both healthy and appealing to consumers. It focuses on three main topics of food storage and preservation technologies, food processing technologies, and the applications of advanced mathematical modelling and computer simulations. This presentation of the latest research and information is particularly useful for people who are working in or associated with the fields of agriculture, the agri-food chain and technology development and promotion

Climate Change Impacts on The Uruguayan Dairy Sector by 2050

This thesis analyzed the impacts of climate change on the Uruguayan dairy sector from today to the year 2050. During the thesis’s development, the application of Climate-Smart Agriculture (CSA) policies was evaluated to enhance the sustainable intensification of this key sector. The four spheres (or fields) of sustainability were considered: economic, socio-cultural, environmental, and organizational. The primary focus was, however, on the economic and environmental effects of climate change and the adaptation and mitigation to its likely impacts. In this context, a novel Rational Holistic Planning and Decision-making Methodology was used to examine the current situation and future scenarios to 2050. A core component of the methodology was the application of the Land Suitability Analysis (LSA) method to the main pastures in Uruguay - Lucerne, and Ryegrass - for comparing their yields in a baseline scenario with projected yields under the expected climate by 2050. CSA relevant practices were then considered to respond to the likely climate changes and generate an approach for the ongoing adaptation of the dairy sector. Finally, different sustainable development indicators were proposed in order to measure the outcomes of the application of CSA policies. The LSA results showed that climate changes by 2050 would impact the suitability of the land to produce Lucerne in Uruguay. A noticeable projected decline is likely to occur mostly in the northeast and northwest of the country. The LSA modeling also indicated that areas in the south and southeast of Uruguay would experience a slight increase in their potential to grow this pasture. In the case of Ryegrass, the LSA modeling indicated that the southeast of the country would be the most benefited by the changes in the rainfall patterns and the increase in temperatures, with some benefits also occurring in the north. On the other hand, the southwest of the country is expected to slightly decrease the suitability for Ryegrass. This demonstrated the diverse impacts of climate change on the two main pastures as well as the possibilities for adaptation; for example, by moving from cultivating one (Lucerne) to the other (Ryegrass) in the southeast and north of Uruguay. These results are an important contribution to the decision-making process of dairy farmers and public institutions promoting the sustainable intensification of the dairy sector towards the future. While this particular research was focused on the Uruguayan dairy sector, the methodology deployed and its key methods can be applied in Uruguay, or other developing countries or sectors, promoting the sustainable development of other industries and regions.Agencia Nacional de Investigación e Innovació