A Powerful Tool
Consider, for example, the supply chain of energy and feedstock raw materials as well as the material handling and storage equipment required to transport and inventory materials before use in chemical production. A DES model provides invaluable insights into the reliability of the plant as a whole when it's used to stochastically analyze the impact of probabilistic failures associated with suppliers, procurement processes, logistics (rail, trucks, etc.) and equipment; uncontrollable events, such as natural disasters, also can be incorporated to observe their long-term effects. Furthermore, by modeling contingency plans for responding to failures, we can accurately identify the vulnerabilities of the supply and recommend areas for improvement. Simulations also can determine the distribution of the probability of plant failure over x number of years, and provide quantifiable justification for projects to improve overall plant reliability. We used a DES model to assess reliability gains from process changes versus capital improvement projects. The model demonstrated the proposed process changes could significantly boost overall plant reliability, making the major capital improvement projects unnecessary.
DES models can also provide insights on the operational requirements of chemical systems and the impact of logistics on these requirements. DES models often are used to answer questions such as:
• What are the effects on chemical production of adding or removing multiple suppliers, transportation, storage and treatment?
• What are the minimum requirements needed to allow for a continuous production outflow?
In addition, DES can enhance Six Sigma process-improvement efforts. Consider, for example, a debottlenecking project. The simulation could address questions like:
• How is the upstream or downstream process affected by this improvement effort? Will the bottleneck move upstream or downstream?
• Will the improved process require additional resources or inventory when seasonality or variability is taken into account?
Simulation also reduces Six Sigma practitioners' reliance on static models or prototypes and post-design rework. This often leads to quantifiable benefits — reduced time and cost — within the product development lifecycle as well as improved Six Sigma project cycle times.
Six Sigma and DES complement each other. Statistical tools support the Six Sigma methodology while simulation provides the next level of statistical rigor, accuracy and robustness required for many complex Six Sigma efforts where error in analysis can't be tolerated [1,2].
Furthermore, DES sometimes is the only viable solution for complex problems such as the optimal scheduling for campaigned products. A simulation-based optimization model enables observing the tradeoffs associated with shorter campaign runs, paired with efficiently set up processes and decreased inventory costs. Optimally scheduled campaign runs bring us one step closer to leaner, more responsive chemical processes.
DES isn't limited to existing operations. Modeling the discrete and continuous elements of a new process as a system can provide important insights for the design of future plants or processes.
A new chemical plant or process design often requires large investments in inflexible and expensive capital assets. Simulating the plant or process is a great way to validate design assumptions, analyze dependencies and analytically quantify initial capital expense as well as the rollout timeframe. Simulation models help in "derisking" capital decisions by providing an early proving ground to test design specifications such as warehousing space, equipment and labor requirements. Moreover, animated simulation models offer a powerful way to visualize a future plant or process and communicate details to stakeholders and others. We developed such a model to test the design specifications of a proposed capital project for a new plant. We used the model to analyze the effect on yearly throughput of varying process times in key equipment, and to determine the impact of increased processing times on warehousing space requirements and the amount of equipment needed. Having this information early in the design phase of a capital project reduces the risk of over- or under-estimating investment.