Mayerton Announced Divestment of Production Facility in China As Part of Strategy

MAYERTON HOLDINGS LTD, a leading refractory engineering solutions provider and a high quality castable and refractories brick manufacturer, announced it signed a definitive agreement for the sale of its 100% equity interest in Dalian Mayerton Refractories Co. Ltd. （ ＂ DMR＂ ） to Magnesita Refratarios S.A. on April 22, 2013. DMR is a refractory brick manufacturing facility in Dalian, China （Liaoning Province）.

Development of Maintenance Management Strategy Based on Reliability Centered Maintenance for Marginal Oilfield Production Facilities

The present work adopted Reliability Centered Maintenance (RCM) methodology to evaluate marginal oilfield Early Production Facility (EPF) system to properly understand its functional failures and to develop an efficient maintenance strategy for the system. The outcome of the RCM conducted for a typical EPF within the Niger Delta zone of Nigeria provides an indication of equipment whose failure can significantly affect operations at the production facility. These include the steam generation unit and the wellhead choke assembly, using a risk-based failure Criticality Analysis. Failure Mode and Effect Analysis (FMEA) was conducted for the identified critical equipment on a component basis. Each component of the equipment was analyzed to identify the failure modes, causes and the effect of the failure. The outcome of the FMEA analysis aided the development of a robust maintenance management strategy, which is based on an optimized mix of corrective, preventive and condition-based monitoring maintenance for the marginal oilfield EPF.

Environmental Impact Assessment of Selected Oil Production Facilities in Parts of Niger Delta, Nigeria

The impact of oil production activities on the chemistry of soil and groundwater was investigated around seven production facilities, ranging from flow stations to wellhead in the western Niger Delta area. The method involved systematic sampling of soil and groundwater within a one kilometre radius of such facilities. The samples obtained were analysed for pH, TOC, TPH, V, Ni and Fe by standard procedures. The results indicate a general conformity of groundwater physico-chemistry to international standards for chemical potability. However, the investigated soil samples reveal in some cases elevated values of TPH (mean: 26.07 mg/kg) and Ni (mean: 8.89 mg/kg) which suggest a negative impact on the soil in the vicinity of such oil production facilities. Although ground-water may show no apparent contamination, pollutants trapped in the soil are in potential transit to groundwater, and may eventually be dissolved and transported through the soil profile to the water table by recharging rainwater. The environmental and health conditions of host communities are thereby endangered.

Maintenance in Marginal Oilfield Production Facilities: A Review

Natural decline in various mainstream oilfield reserves and the high investment capital in upstream exploration and project development have promoted attention towards smaller oilfields referred to as Marginal fields. This provides operators the opportunity to commence exploration and production with minimum requirements of design, installation, and operations. Although the low Capital Expenditure (CAPEX) requirement favors the start-up of marginal oilfield operations, several operators are not able to sustain the field’s operations due to the high Operational Expenditure (OPEX), particularly arising from facilities’ maintenance. The aim of this paper is to review the maintenance strategies adopted in marginal oilfields, assess their effectiveness, and provide a pointer towards efficient and viable maintenance strategies for the sustainability of marginal oilfields. The study showed that time-based preventive maintenance is predominant in the oil industry, which constitutes up to 40% of net operational expenses. In other cases, reactive maintenance is adopted, which often results in an unplanned shutdown, known to be responsible for nearly half of the overall losses of an oil facility. A paradigm shift in maintenance to Reliability Centered Maintenance (RCM) was explored for marginal oilfield, with a comprehensive review of various maintenance strategies, ranging from maintenance optimization strategies, Heuristics and Metaheuristics, Artificial Intelligence (AI), and Data Mining techniques. It was observed that the application of AI best addresses the proposed RCM for marginal oilfields. This was drawn from the recorded limitations of the other concepts from verifiable similar works, where different AI techniques and Data analytics methods have been successfully applied to aid RCM.

Online diagnosis platform for tomato seedling diseases in greenhouse production

The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform consists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall construction was designed to be compressed and lightweight;Secondly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.Compared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest control research and the development of smart agriculture.

A BRIEF INTRODUCTION TO CNAIC'S PRODUCTION FACILITIES

Tel: (0434) 388725 Fax: (0434) 388958 Add: 54, Pingdong Rd., Siping, Jilin 136001 Date of Foundation: 1965 General Manager: Bian