This paper provides background on, and an overview of, the soon to be released WBF XML batch and enterprise-control system schemas. The schemas consist of two sets, one is intended to provide for the exchange of batch data and is based upon the ANSI/ISA 88 standard. The second is intended to serve as a basis for exchanging data between enterprise and control systems and is based upon the ANSI/ISA 95 standard. The organization of each set is described along with examples on how they can be used.
This paper describes the application of an advanced model predictive adaptive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult
problem for many operators of these processes. Temperature control on these systems is difficult for conventional Proportional-Integral-Derivative (PID) controllers because the response is characterized bynan open loop integrator with long delay and time constant. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products and reaction rates can be highly temperature dependent. The applications discussed in this paper include a PVC reactor and an Ethoxylated fatty acid reactor. In each case, the variability of the reactor temperature was reduced by 60% or more. Improved temperature control permitted operation at higher reaction temperatures with higher sustained feed rates of reactants and catalysts while remaining within product temperature limits. Batch cycle times were reduced by as much as 35% due to the higher sustained reaction rates. The applications demonstrate the attractive economics for optimization of batch reactors with model predictive controls and highlight the opportunity for tremendous improvements in batch consistency, reduced batch cycle times, and improved productivity.
Mihai Huzmezan, University of British Columbia, Pulp and Paper Centre; Bill Gough, Sava Kovac, Universal Dynamics Technologies Inc.
The crucial task for engineers involved in designing manufacturing facilities is to determine the most efficient way to dimension process cell equipment to achieve the required production capacity when beginning with general recipes. In case of technology transfer, the challenge is the assessment of facility production capability to perform the process requirements, being defined in the general recipe. This paper suggests a useful method for determining process cell equipment and equipment functionality based on number of products, their quantity and general recipe data. In case of technology transfer to other sites or cells, it is suggested how to assess available production capability, based on general recipe data and installed equipment capability. The suggested method can be useful during multiproduct facility dimensioning and, in case of technology transfer, the assessment of existing facilities. The result of implemented method is the best way to optimal price/performance ratio, in case of investment decisions, and reduction of life-cycle engineering efforts.
Marin Klaric, Business Development Specialist, PLIVA
Continuous control of batch reactors is now feasible with Model Based Predictive Control. First Principles modeling allows the solution of difficult problems of non linear, integrative, constrained, cascaded end split range control, with no lag error on ramping set points. This has been implemented in DCS control boards and PLC,s.Diverse industrial applications are described. Linking Sequential Control and Continuous Control is made easier if both control schemes are implemented in the same generic control library.
J. Richalet, ADERSA; Eric Vitté, Schneider Electric
Batch control projects have tended to be software intensive and often overrun substantially. In most cases this is due to a lack of foresight and planning in the early stages. The S88 Era has tried to alleviate this situation by providing a structured methodology but again the flexibility of current control systems and a lack of planning can produce the same effect as having no structure at all. This paper aims to highlight some potential problems and pitfalls and how they may be contained without adversely affecting the outcome of the project.
Dr. Maurice J. Wilkins, Director of Process Automation and Control Systems, Millennium Specialty Chemicals
New technologies bring a step change in the availability, flexibility and quality of data for reporting. Batch reporting can be considered to be made up of three elements (1) automated actions by the control system, (2) manual actions and records by an operator and (3) process values. Windows technology has made the combination of (1) and (2) routine but combining (1), (2) and (3) into a single report has been elusive. In addition within the regulated industries the integrity of such a report has to be assured.
The ISA 95 Enterprise to Manufacturing integration model is used to structure the integration processes between business systems and the plant floor. Through the use of the ISA 95 structures, a common denominator business model was established for integrating SAPs R/3 PP-PI transactions and data structures with an ISA 88 batch automation data structure.
Quality and consistency are key factors in determining business success. Manufacturing products that satisfy product quality and consistency specifications first time result in increased productivity and lower overall manufacturing costs. Approaches to achieving consistently high quality production and enhanced manufacturing performance include Statistical Process Control (SPC) and Six Sigma with increasing attention now being paid to Multivariate Statistical Process Control methodologies (MSPC) or perhaps better termed Process Performance Monitoring. In todays process manufacturing environment, a number of issues arise which can challenge the application of MSPC based process performance monitoring technologies. For example, most applications of MSPC have tended to focus upon the manufacture of a single product, i.e. one grade, one recipe, etc. with separate models being developed to monitor individual product types. However, with process manufacturing trends being influenced by customer demands and the drive for product diversification, there has been an increase in flexible manufacturing. Thus with many companies now producing a wide variety of products, there is a real need for process models which allow a range of products, grades or recipes to be monitored using a single process representation. Three industrial case studies are presented to demonstrate the application of the multi-group performance monitoring approaches.
The operations and manufacture of biopharmaceuticals is a complex process combining the capabilities of multiple systems that extend the boundaries of batch processing. The Manufacturing Execution System (MES) receives information from the Enterprise Resource Planning (ERP) system and creates the necessary production orders, maintains material tracking/genealogy and coordinates key manual activities. The automated batch control system sequences the phases, controls the devices and captures the necessary history. These systems come together in the operation of Biopharmaceutical production plants, which require a very specific architecture that leverages standard batch products that are tightly integrated with MES capabilities. This is driven by the upstream and downstream processing specifications of such plants, the detailed compliance requirements and the benefits achieved in maximizing automated functionality. This paper explores the unique requirements of batch manufacturing in the biopharmaceutical environment.
Batch processes depend heavily on the speed and repeatability with which each material transfer is completed for every recipe executed. Each and every transfer generally requires precise cut-off control over the valves, screw feeders or pumps, as those transfers directly impact the annual profitability of a manufacturing facility. Therefore it is very important to have a cost-effective material transfer control system that consistently improves process quality and throughput while reducing raw material waste and operating costs.
This paper presents some main factors to impact speed and accuracy of batch material transfers. In addition to functions to reach the goals of speed and accuracy for batch material transfers, many other either must-have, should-have or beneficial functions are explained. Where those functions should be built? Some considerations are presented to answer the question in this paper.
Control modules used for critical phases of reactor operation such as heating, cooling and reacting can be optimized using advanced process control technology to reduce batch cycle time. Temperature control of batch reactors is difficult for conventional proportional-Integral-Derivative (PID) controllers due to the open loop instability of these processes coupled with the long time delays and large time constants. These dynamics are present on various reactor designs involving heating or cooling with jackets, internal coils, or recirculation loops through external heat exchangers. Model Predictive Control (MPC) provides an alternative to PID for use in these control modules to dramatically improve temperature set point tracking, improve product consistency, and reduce batch cycle time. This paper describes the design of an MPC controller that is built to specifically handle the dynamics found on batch reactors as well as the large process disturbances that occur due to exothermic reactions. The results of an application example will be discussed.
Bill Gough, Sava Kovac, Lynne DeVito, David Quick, WBF
The most successful biotech companies have multiple products approved for market and must make the use of existing manufacturing capacity to produce them. Often times this requires rapid changeover from one product to the next. This paper addresses the challenges with bringing new products into an existing S88.01 facility and the challenges involved in maintaining the standard as well as implementing a change in an operating plant with a minimum of downtime. It was found that the S88 concept is enormously helpful in implementing changes within the equipment capability but that challenges arise when the equipment capability must be changed also. The recommendation is to standardize the manufacturing process and build the necessary capability into the plant upfront to avoid costly downtime during product changeover.
The application of Model Predictive Control (MPC) is often considered for multi-variable continuous processes. However, the benefit of applying MPC to a batch process can often be just as significant as a continuous process. In this presentation we will show how MPC is being applied in a pharmaceutical manufacturing facility for the control of one cut of a batch distillation column. The primary benefit of using MPC is to reduce batch cycle time. The performance that can be achieve with MPC vs. traditional techniques for this application will be examined.
After years of advancement in the batch control industry--particularly within the area of standardization using S-88 and S-95--todays automated batch control systems offer end users many features which have enabled improvements in product quality, reduced cycle time, and overall return on investment. However, the area of automated batch scheduling and MES integration has to date gone largely untapped on a relative basis when comparing features incorporated into the actual control system. In general, the problem of batch scheduling and MES integration has been left to customized, one-off solutions developed on a site-by-site basis (due to both plant variability and lack of support in the underlying control system). This paper will address recently developed technologies that begin to incorporate the batch scheduling and integration layers into the control system itself, while still maintaining the flexibility required for customization of the already present hierarchical architecture. Along with a description of the solution, real world case studies will be discussed, including both results and lessons learned.
The ability to share information between batch control systems and Manufacturing Execution Systems (MES) or Enterprise Resource Planning (ERP) systems has become a critical parameter in adapting to the expanded requirements for agile manufacturing. Traditionally, batch data and execution control has been the domain of control engineers. MES and ERP Systems have lived in the information technology environment with data exchanged manually with the plant floor. This increases the chance of data entry errors and slows down access to process information needed for effective scheduling, material tracking, reporting and decision-making.
All over the world, small and medium sized plants in the chemical, foods and beverage industries operate in manual or "manumatic" mode. For a small batch process operation to achieve the same level of excellence as larger companies, it must establish batch best practices. Batch manufacturing is a sequential processing activity at the level of process cells. In these cells, control modules are applied to equipment modules to run a recipe in a specific unit. To establish a best practice it is necessary to link it to a determinate Process Cell and make the right choice in process modularization. An example process cell is postulated and a possible process material selection control module discussed. Some alternatives are compared and some fundamental parameters identified to be optimized in the best batch sense.
Batch software upgrades on validated batch applications raises many issues. This paper talks about guidelines on how much revalidation evidence is needed to meet regulatory requirements for validated batch applications, if changes made to software that is utilized in developing these automated applications. In addition, the benefits and liabilities expected after making upgrades to batch software.
Often, it is difficult to estimate the level of testing effort needed to perform on a production system after implementing software upgrades. The outcome of this testing/documentation should provide sufficient data to demonstrate that software upgrades have no negative impact on equipment/product/process performance and the system has been restored to its validated status. This is the absolute requirement for Pharmaceutical Companies and should meet the guidelines required by FDA and internal company standards.
This paper discusses the automation of the batch process and raw materials warehouse functions at a large perfume manufacturing facility. Many items no longer met the business needs of the facility, including: manual processes, insufficient reporting, and minimal information distribution.
The paper will present a new methodology for using predictive control to improve batch process outcomes. Predictive control refers to a situation in which data collected during the execution of earlier process stages are used to retarget controllable variables during later process stages. Improvement is measured either by increasing output yield or by shifting the distribution of product outputs towards higher bin values, thereby increasing the value of a given output quantity. The technology includes a graphical method of presenting the logical flow of causality and functionality along a process. The paper describes how this methodology has been used in order to improve production outcomes in a batchbased system. The first part of the paper will present the technology, its pros and cons relatively to the traditional S88 without predictive control, and how the adaptive system is integrated within control systems. The second part of the paper will present results from a specific case study in which this technology has been implemented.
Batch control analysis is one of steps in the design of plants executing batch process - an S88-based analysis of batch process from the control point of view. It is between the conceptual design of the batch process (PFD or P&I) and the start of the design of the control system (design of instrumentation, control hardware and software). Its objective is to fill the gap almost always found between the formulation of requirements and the specification of the actual implementation. It transforms user requirements into process-based detailed functional requirements. It has several advantages if these problems are handled, not by software engineers, rather by batch control analysts who are closer to the plant and know the process better.