Simulation

Modelling and simulation is having a significant role in improving the competitiveness of industry. It has become an important enabling technology in decision-making, engineering and operation, covering the whole life span of a manufacturing facility.

In the past, specific tailor-made simulators were developed from scratch and used by mathematicians and programmers making simulation very time-consuming and expensive. The new generation of simulators is dedicated for use by professionals in different application fields. The intelligent modelling environment will include process designer's knowledge, to assist the casual user. The modelling is rapid, convenient and cost effective.

Simulation is used to evaluate the design of process components and separate unit operations. Model specifications are re-used in the design evaluation of large integrated processes and control systems. In addition, the models once made are used to support operator training. Computerized dynamic simulation models are useful for verification of both conceptual and detailed process designs. They make in-house pre-testing of automation systems, user interfaces, and operational procedures possible, as well. They are used for generic teaching and learning of basic principles, detailed pre-training of new personnel, and re-training of experienced operators.

The History

Models in a broad sense have been used for a long time in the history of mankind. The oldest models are probably pictures and sculptures that have been crafted for religious purposes. The first mathematical models were possibly introduced to find out the movements of planets around the sun. Different methods were developed to solve differential equations.

At the end of 19th century Lord Kelvin presented the theory of how an integrator in a feedback loop is used to simulate continuous systems. In the 1920s the same principle was used to solve ballistic equations for calculating firing tables using an analog computer. Simulation technology really started to take off when the operational amplifier was invented after the Second World War. Analog computers used operational amplifiers as calculating elements. Due to the improved production methods of operational amplifiers, analog computers were commercially available in the 1950s. To increase the calculation capacity the analog computer was augmented by a digital computer. This was called a hybrid computer. During the 1980s and 1990s the computing power of digital computers was rapidly increasing which made analog computers obsolete also in simulation technology.

The origin of the discrete event simulation is in handling of probabilistic problems that rose in gambling. In case of complex games, one had to satisfy in observing experimental frequency. On a broader scale, discrete event simulation was used in the development of nuclear weapons to calculate the average path length of neutrons using different probabilities for reflection and absorption. This evolved the Monte-Carlo method - well known in the fields of applied mathematics and operational research. New effective modelling environments have been introduced thanks to the development of digital computers and programming. The discrete event simulation is nowadays extensively used to improve complex production and logistic processes.

Motivation and Use

Simulation is extensively used in a situation when the real system cannot be used for experiments. This is the case for example when:

• The real system does not yet exist.
• The experiments would involve high economical risks.
• The experiments would be dangerous.
• The experiments cannot be controlled or carried out.
• The process variables cannot be measured.
• The measurements are too noisy.
• The experimenting with the real system is expensive.

Substantial benefits can be achieved when simulation is used in design of new production facilities. Simulation is used to try out different options and to produce data for decision making. The experimenting with full-scale equipment is often difficult and expensive. In those cases simulation is used to produce the information needed. In following cases the experimenting with a real system is impossible:

• The system cannot be accessed.
• The dynamics and response of the system is too slow.
• The proper conditions for the experiment cannot be fulfilled.
• The variables of the system cannot be manipulated.

Simulation is used to better understand the behaviour of the real system and the dependencies of system parameters and variables. When simulation model is build the assumptions and information must be sorted and classified. Missing pieces of information are easily identified. Simulation experiments are run during modelling process and intuitive information is gathered. Simulation model is thus used in education when there is a need to teach how a complex system behaves and how it is controlled.

Simulation has been used in various manufacturing sectors and applications shown in the table.

Economic Impact

Using simulation-based methods to improve manufacturing has significant benefits. The benefits are achieved in terms of cost savings and intangible assets like deeper understanding of the manufacturing process. The payback time of a typical improvement project is shorter than two years - in some cases only a few months. Usual benefits of applying simulation are:

• Increased confidence in big decisions
• Higher quality of the manufacturing facility design - no costly mistakes
• Shorter commissioning time of a new production facility
• Increased plant availability - operation at full capacity - ability to deliver on time in full
• Savings in training costs of personnel
• Reduced plant damage
• Improved environmental management
• Extension of equipment life.

Please contact us at info@sim-serv.com to find out how simulation can help your business.