Safety and Risk Management
Genetic programming-based development of thermal runaway criteria
Common thermal runaway criteria (e.g. divergence criterion and the Maxi criterion) may predict a thermal runaway unreasonably as the Maximum Allowable Temperature (MAT) is not taken into account. This contribution proposes a method for the goal-oriented construction of reactor runaway criteria by Genetic Programming (GP). The runaway prediction problem is formulated as a critical equation-based classification task, and GP is used to identify the optimal structure of the equations that also take into account the MAT. To demonstrate the applicability of the method, tailored criteria were developed for batch and continuous stirred-tank reactors. The resultant critical equations outperform the well-known criteria in terms of the early and accurate indication of thermal runaways.
Test-sequence optimisation by survival analysis
Testing is an indispensable process for ensuring product quality in production systems. Reducing the time and cost spent on testing whilst minimising the risk of not detecting faults is an essential problem of process engineering. The optimisation of complex testing processes consisting of independent test steps is considered. Survival analysis-based models of an elementary test to efficiently combine the time-dependent outcome of the tests and costs related to the operation of the testing system were developed. A mixed integer non-linear programming (MINLP) model to formalize how the total cost of testing depends on the sequence and the parameters of the elementary test steps was proposed. To provide an efficient formalization of the scheduling problem and avoid difficulties due to the relaxation of the integer variables, the MINLP model as a P-graph representation-based process network synthesis problem was considered. The applicability of the methodology is demonstrated by a realistic case study taken from the computer manufacturing industry. With the application of the optimal test times and sequence provided by the SCIP (Solving Constraint Integer Programs) solver, 0.1–5% of the cost of the testing can be saved.
Reliability - Redundancy Allocation in Process Graphs
Process graphs (P-graphs) have been proven to be useful in identifying optimal structures of process systems and business processes. The provision of redundant critical units can significantly reduce operational risk. Redundant units and subsystems can be modelled in P-graphs by adding nodes that represent logical conditions of the operation of the units. It is revealed in this paper that P-graphs extended by logical condition units can be transformed into reliability block diagrams and based on the cut sets and path sets of the graph a polynomial risk model can be extracted. Since the exponents of the polynomial represent the number of redundant units, the cost function of the reliability – redundancy allocation problem as a nonlinear integer programming model can be formalised, where the cost function handles the costs associated with consequences of equipment failure and repair times. The applicability of this approach is illustrated in a case study related to the asset-intensive chemical, oil, gas and energy sector. The results show that the proposed algorithm is useful for risk-based priority resource allocation in a reforming reaction system.
Decision tree and first-principles model-based approach for reactor runaway analysis and forecasting
Decision trees (DTs) are effective in extracting linguistically interpretable models from data. This paper shows that DTs can also be used to extract information from process models, e.g. they can be used to represent homogenous operating regions of complex process. To illustrate the usefullness of this novel approach a detailed case study is shown where DTs are used for forecasting the development of runaway in an industrial, fixed bed, tube reactor. Based on first-principles knowledge and historical process data the steady-state simulator of the tube reactor has been identified and validated. The runaway criterion based on Ljapunov's indirect stability analysis has been applied to generate a data base used for DT induction. Finally, the logical rules extracted from the DTs are used in an operator support system (OSS), since they are proven to be useful to describe the safe operating regions. A simulation study based on the dynamical model of the process is also presented. The results confirm that by the synergistic combination of a DT based on expert system and the dynamic simulator a powerful tool for runaway forecasting and analysis is achieved and it can be used to work safe operating strategies out.
Novel Method for the Determination of Process Safety Time
Nowadays process safety is a key issue at the design and operation of a production process. Unfortunately, at the application of process hazard analysis (PHA) techniques the time aspect of operation and the dynamical behaviour of the process are neglected. This is due to the small number of easily available tools which can be applied to perform dynamical process simulation and dynamical analysis. However, in recent times dynamical models are increasingly applied to support the solution of any tasks related to process safety. Another problem that makes it difficult to take into account time, is the lack of a standardized concept and evaluation system to integrate the obtained information into the design procedure of safety integrated system (SIS). The aim of this article is to investigate the role of time in the design of process safety elements (PSEs), and to define the connection between the process and the time by using the process safety time (PST) term, as well as to give a methodology how PST can be designed based on the process simulator and applied in the development of PSEs. The developed methodology is based on dynamical analysis of the system and the possible safety actions. Based on this methodology, an algorithm has been developed to detect unsafe situations and to determine the necessary safety actions that can be used to avoid the undesired states of operation. The algorithm has been applied in the solution of an industrial problem related to reactor runaway.
Detection of Safe Operating Regions - a Novel Dynamic Process Simulator Based Predictive Alarm Management Approach
The operation of complex production processes is one of the most important research and development problems in process engineering. A Safety Instrumented System (SIS) performs specified functions to achieve or maintain a safe state of the process when unacceptable or dangerous process conditions are detected. The safe state is a state of the process operation where the hazardous event cannot occur. The set of safe states definene safe operating regions. A logic solver is required to receive the sensor input signal(s), make appropriate decisions based on the nature of the signal(s), and change its outputs according to userdefined logic. Next, the change of the logic solver output(s) results in the final element(s) taking action on the process (e.g. closing a valve) to bring (back) it to a safe state. Alarm management is a powerful tool to support the work of the operators to control the process in safe operating regions and detect process malfunctions. Predictive alarm management systems should be able not only to the early detection of the alarm, but also to give advice to process operators which safety action (or safety element(s)) must be applied. The aim of this paper is to develop a novel methodology and toolkit to support these tasks. The essential of the proposed methodology is the simulation of the effect of safety elements over a prediction horizon. Since different manipulations have different time demand to avoid the evolution of the unsafe situation (safety time), the process operators should know which safety action(s) should be taken in a given time. For this purpose a method for model based predictive stability analysis has been worked out based on the Ljapunov's stability analysis of simulated state trajectories. The introduced algorithm can be applied to explore the stable and unstable operating regimes of a process (set of safe states), which information can be used for predictive alarm management. The developed methodology has been applied in two industrial benchmark problems related to the thermal runaway of these reactors.