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In the industries across the petroleum chain and those involved in energy generation, the use of petroderivatives as fuel oils is common. To clean parts, equipment and environments contaminated by hydrocarbons, they use expensive, toxic products, bringing risks to the environment as well as to workers’ health. Thus, the aim of this study was to check the stability of a biodetergent prepared using atoxic substances for large-scale production and industrial energy sector application. The relationship between volume (4 to 10 L) and stirring time (5 to 10 min) of the formulation at 3200 rpm and 80 °C was evaluated. The hydrophilic lipophilic balance (HLB), long-term stability (365 days), toxicity and efficiency of low-sulfur, viscous fuel oil removal from metal pieces and floors were investigated. The interaction among operating conditions was shown to influence the features of the product, which achieved approximately 100% stability after a stirring time of 7 min. The emulsion HBL index varied between 4.3 and 11.0. The biodetergent maintained its physicochemical properties during its 365 days of storage and showed high efficiency, removing 100% of the OCB1 impregnated on the metallic surfaces and floors tested. The formulation showed reliability in scale up when submitted to the study of physicochemical factors in the productive process, and safe application, by reducing risks for workers’ health and environment.

Thermoelectric power plants consist of a set of critical equipment that require high levels of availability and reliability. Due to this, maintenance of these physical assets is gaining momentum in industry. Maintenance is considered as an activity that contributes to improving the availability, efficiency and productivity of each piece of equipment. Several techniques have been used to achieve greater efficiencies in maintenance, among which we can find the lean maintenance philosophy. Despite the wide diffusion of lean maintenance, there is no structured method that supports the prescription of lean tools applied to the maintenance function. This paper presents the experience gathered in two lean maintenance projects in thermoelectric power plants. The application of lean techniques was based on using a previously developed multicriterial decision making process that uses the Fuzzy Analytic Hierarchy Process (AHP) methodology to carry out a diagnosis and prescription tasks. That methodology allowed the prescription of the appropriated lean techniques to resolve the main deficiencies in maintenance function. The results of applying such lean tools show that important results can be obtained, making the maintenance function in thermoelectric power plants more efficient and lean.

This paper presents a specific method to improve the reliability of the equipment and the quality of power supplied to the electrical systems with the frequency and voltage control of a thermoelectric plant, to guarantee a more stable system. The method has the novelty of combining Total Productive Maintenance (TPM) using only four pillars, with Electrical Predictive Maintenance based in failure analysis and diagnostic. It prevents voltage drops caused by excessive reactive consumption, thus guaranteeing the company a perfect functioning of its equipment and providing a longer life of them. The Maintenance Management Program (MMP) seeks to prevent failures from causing the equipment to be shut down from the electrical system, which means large financial losses, either by reducing billing or by paying fines to the regulatory agency, in addition to prejudice the reliability of the system. Using management tools, but applying only four TPM pillars, it was possible to achieve innovation in power plants with internal combustion engines. This study aims to provide maintenance with a more reliable process, through the implantation of measurement, control and diagnostic devices, thus allowing the management to reduce breakdown of plant equipment. Some results have been achieved after the implementation, such as reduction of annual maintenance cost, reduction of corrective maintenance, increase of MTBF (Mean Time between Failures) and reduction of MTTR (Mean Time to Repair) in all areas. Probabilistic models able to describe real processes in a more realistic way, and facilitate the optimization at maximum reliability or minimum costs are presented. Such results are reflected in more reliable and continual power generation.