Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Benefits of using IMPL


Published on

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Benefits of using IMPL

  1. 1. The Benefits of IMPL (IMPL-Benefits) “Finding Profitable, Performance-Rich & Penalty-Free Solutions to Process Industry Problems” i n d u s t r IAL g o r i t h m s LLC. (IAL) August 2014 Our Industrial Modeling and Programming Language (IMPL) is an optimization enabler to help reduce the barrier to implementing advanced decision-making and data-mining applications in industrial on- and off-line environments. We strongly believe that making better decisions using better data will considerably improve your business’ bottom-line especially inside the production-chain (Kelly, 2004b, 2005) where complexity issues are the least understood. The combination of making good decisions using good data is well-known and is part of the continuous-improvement revolution also known as Kaisen in Lean Manufacturing. When poor data is used to make decisions then larger than expected “plan versus actual” deviations or steady-state offsets result which increases the overall uncertainty of the system and erodes the likelihood of making the best decisions possible (Kelly, 2000 and Kelly and Zyngier, 2008). At the core of IMPL is its ability to maximize profit (hard benefits) and performance (soft benefits) and to minimize penalties (i.e., infeasibilities and inconsistencies if they exist). It is typical to think of planning as optimizing the economics of the plant (revenues minus costs) (Kelly, 2004a) whereby scheduling as optimizing the efficiency and effectiveness of the plant (Zyngier and Kelly, 2009). However, unique to IMPL is its ability to combine both planning and scheduling objectives into the same optimization problem. This not only allows IMPL to model and solve either a planning or a scheduling problem but also to optimize a problem with both planning and scheduling details when required to capture all of the benefits of better decision- making (Menezes, Kelly, Grossmann and Vazacopoulos, 2015). In addition, IMPL also has a novel feature called “planuling” which is a portmanteau of planning and scheduling whereby faster processes are planned and slower processes are scheduled all within the same time- horizon. These types of situations are commonly found in the process industries such as in oil- refining where the blending operations are faster than the conversion and separation processes and in bulk and specialty chemicals where the production-lines are slower than the packaging- lines. These kinds of innovations enable longer time-horizons with shorter time-period durations for instance and can increase the amount of decision-making look-ahead which is always a useful endeavor to mitigate against uncertainty which is a key element in rolling, receding or moving horizon advanced process control. A relatively simple but qualitative approach to articulate some of the potential benefits of better scheduling with optimization, in contrast to scheduling with simulation (spreadsheets), is to consider a quantity-quality trade-off curve with logistics isotherms as shown in Figure 1 (Kelly, 2006). In this figure, the gray line is the current operating, manufacturing or production-line using spreadsheets with simplified or basic logistics. The term “logistics” relates essentially to the degree of sophistication, flexibility and/or agility of the plant’s operation such as using dedicated tanks when they are in fact multiproduct (Kelly, 2006) or using product-wheels instead of explicit sequence-dependent changeovers (Kelly and Zyngier, 2007). The red-dotted line is the production-line using optimization with detailed or advanced logistics. The blue production- line represents the ideal production which is never achievable but should always be strived for.
  2. 2. Figure 1. Quantity-Quality Trade-Off Curve with Logistics Isotherms (Kelly, 2006). If we replace manual scheduling (simulation) with automated scheduling (optimization) then we can expect to move from the gray to the red-dotted line. For the same quantity, an improved level of quality can result which will translate into producing more valuable product and hence raising the profit provided the market can accept the improved quality. For the same quality, an increased level of quantity can be produced also raising profit provided the market can absorb the increased quantity. A more quantitative approach to assessing the benefits of better scheduling optimization can be found in Kelly and Mann (2003) which also has better planning implications as well. The case study was from a medium-sized and integrated Exxon oil-refinery (100K BPD) in Canada were crude-oil blend scheduling optimization was applied with better crude-oil to tank segregation based on the true quality bottlenecks of the plant i.e., diesel stream sulfur instead of crude-oil bulk sulfur (Kelly and Forbes, 1998). Figure 2. Quality Variability Reduced Through Better Scheduling (Kelly and Mann, 2003).
  3. 3. Before scheduling optimization was installed, manual scheduling (first part of Figure 2) using a spreadsheet resulted in a large variance or standard-deviation between the monthly planning crude-oil sulfur target and its day-to-day actual value. An absolute maximum limit for the crude- oil sulfur flow was set by the downstream sulfur plant capacity and used to determine a proxy target for the crude-oil bulk sulfur concentration. Due to the frequent spiking of the sulfur and given the maximum sulfur content bound, the planners unfortunately backed-off on the sulfur target in their planning optimization software thus artificially reducing the plant’s capability due to poor manual scheduling. Once better crude-oil scheduling was installed and the precision increased, the back-off amount was steadily reduced (i.e., the sulfur target increased) in the planning and this resulted in hard benefits of over $10M USD per year given today’s crude-oil costs. This an excellent example where maximizing performance or improving the soft benefits can directly impact the profit or hard benefits of installing better scheduling optimization. In another recent and similar application of downstream jet, ultra-low sulfur diesel (ULSD) and heavy fuel oil blending at a major oil-refinery in Europe (Kelly, Menezes and Grossmann, 2014), the hard benefits were estimated as a payback of less than month’s worth of operation! In summary, it should be clear that the benefits of better advanced planning and scheduling (APS) optimization are considerable and tangible and in many ways are similar to how benefits are calculated when justifying and auditing advanced process control and optimization (APCO) projects. And finally with IMPL, we believe that your time to achieve these hard and soft benefits as well as to capture all of the benefits, will be drastically improved when IMPL’s pre- configured discrete and nonlinear models and state-of-the-art solving techniques are chosen for your next advanced decision-making project. Please contact Alkis Vazacopoulos ( to obtain a quote for both IMPL’s development and deployment licences as well special pricing for IBM’s CPLEX LP, QP and MILP solvers which are tightly integrated with IMPL to solve industrially significant discrete and nonlinear types of problems. References Kelly, J.D., Forbes, J.F., “Structured approach to storage allocation for improved process controllability”, American Institute of Chemical Engineering Journal, 44, 1832-1840, (1998). Kelly, J.D., “The necessity of data reconciliation: some practical issues”, NPRA Computer Control Conference, Chicago, IL, (2000). Kelly, J.D., Mann, J.M., "Crude-oil blend scheduling optimization: an application with multi- million dollar benefits", Hydrocarbon Processing, June, 47, July, 72, (2003). Kelly, J.D., "Formulating production planning models", Chemical Engineering Progress, January, 43, (2004a). Kelly, J.D., "Production modeling for multimodal operations", Chemical Engineering Progress, February, 44, (2004b). Kelly, J.D., “Modeling production-chain information”, Chemical Engineering Progress, February, (2005).
  4. 4. Kelly, J.D., "Logistics: the missing link in blend scheduling optimization", Hydrocarbon Processing, June, 45, (2006). Kelly, J.D., Zyngier, D., "An improved MILP modeling of sequence-dependent switchovers for discrete-time scheduling problems", Industrial and Engineering Chemistry Research, 46, 4964, (2007). Kelly, J.D., Zyngier, D., "Continuously improve planning and scheduling models with parameter feedback", FOCAPO 2008, July, (2008). Zyngier, D., Kelly, J.D., "Multi-product inventory logistics modeling in the process industries", In: W. Chaovalitwonse, K.C. Furman and P.M. Pardalos, Eds., Optimization and Logistics Challenges in the Enterprise", Springer, 61-95, (2009). Kelly, J.D., Menezes, B.C., Grossmann, I.E., “Distillation blending and cutpoint temperature optimization using monotonic interpolation”, accepted in Industrial and Engineering Chemistry Research, August, (2014). Menezes, B.C., Kelly, J.D., Grossmann, I.E., Vazacopoulos, A., “Generalized capital investment planning of oil refinery units using MILP and sequence-dependent setups”, submitted to Computers and Chemical Engineering, (2015).