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Early-warning for leakage in a recovery boiler can help the process operator to detect faults and take action when a dangerous situation is developing. By analysing the mass-balances on both the steam and combustion side of the boiler in a Bayesian network, the probability of leakage can be determined and used as an early-warning. The method is tested with real plant data combined with leakage simulations. The results show that it is possible to detect considerably smaller leakages using this method than using the type of simple steam-side mass-balance method that is in use today. Bayesian network is an efficient tool to combine information from measurement signals and calculations giving an early-warning system that is robust to signal faults.

Operating and diagnosing complex industrial processes are usually difficult tasks. Faults that cause only minor disturbances in the production can be frequent. It is mainly the operator who detects, isolate and take action when a disturbance or fault appears. It can be very hard to distinguish between normal process operation with its minor disturbances and a developing fault. Early-warning systems can help the process operator to detect faults at an early stage and thus prevent further fault development. Early-warning systems have been developed for safety critical events, e.g. fire on ships [1], disconnections in electrical systems [2] and landslides [3]. Common for early-warning systems are that they are designed to make a person aware of that a critical situation is or might be developing. Industrial processes normally have alarms on signal levels to pay attention to large deviations from a normal value. The alarm trigging levels are set to protect the process equipment, maintain product quality, etc. A diagnostic system can give the process operator more information than just an alarm that a signal is deviating. Fault diagnosis is commonly divided in the two steps: detection; and isolation [4]. Fault detection is to determine that there is a fault and isolation is to determine where the fault is located. An early-warning system is a diagnosis system aimed to help the process operator to detect and isolate faults as early as possible. Leakage detection in recovery boilers is important to avoid severe damages on equipment. The walls of the furnace are containing evaporating water with high pressure. Fireside corrosion and thermal stress can cause leakages, implying that water or steam comes in contact with the smelt. Water in the smelt can cause an explosion with total destruction of the boiler as result. There are a number of commercial systems for detection of leakage flows in both conventional boilers and recovery boilers [5] and [6]. These systems are detecting the leakage flow or cracking with acoustic sensors. In this work, a new method to detect boiler leakage flows by a mass-balance in a Bayesian network is evaluated by study of a typical recovery boiler. Data from real process operation are combined with a process model to simulate leakage flows. Bayesian networks [7] and [8] have been used for diagnostics in many different areas, e.g. medicine, electronics and mechanics [9]. An advantage over other diagnostic methods is the handling of uncertainty. An alternative would be to use, e.g. fuzzy logic [10]. This methodology will be investigated in future research on the current application.

Early-warning for fault detection in a recovery boiler was considered. A methodology based on mass-balances in a Bayesian network was developed. The early-warning system was tested on real plant data from normal operation combined with simulations to derive leakage cases. The results show that normal-load operation gives a leakage probability below 10%. A simulated leakage of 0.25 kg/s gives a leakage probability of about 25%. This means, during normal operation, that an early-warning can be generated when the leakage probability exceeds 10% without generating false alarms. A fault in measurement signals can cause a leakage probability of more than 10%, but only when the relative fault in the signal is large. The detection level and reliability are depending on the measurements on feed-water flow and black-liquor dry substance. A limit for early-warning when the leakage probability exceeds 20% can help the operator to be aware of that something is wrong, possibly a leak in the boiler. Further investigations can then be done to assess if it is a leakage or not. The ability to combine the mass-flow balance with other leakage detection methods in the Bayesian network is a possible way to further improve the warning sensitivity and reliability. The method with mass-balances formulated in a Bayesian network is an efficient tool to analyse the balances and warn the process operator if the leakage probability becomes too high. Integrating signal diagnosis in the network structure makes the system robust to minor signal faults. Early-warning for a recovery boiler is one application in which the method is applicable, but it can also be generalized to other applications, e.g. screen clogging, hang-ups or sintering.