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The purpose of this thesis is the design of the software necessary for teleoperation of a fluid handling laboratory. It does not include the implementation of this software. The laboratory for which it is designed is being developed at the University of Arizona, and is a model of the fluid handling laboratory aboard Space Station. The software includes man/machine, machine/machine, and machine/instrument interfaces. The man/machine interface is menu driven and consists of high level commands which are independent of the devices in the laboratory. The machine/machine interface is also device independent. It consists of intermediary commands and maps the commands of the man/machine interface into the low level, device dependent, commands and programs of the machine/instrument interface. Although the software is primarily designed for the model laboratory, the needs of a remotely operated fluid handling laboratory aboard Space Station have been considered.

A fluidized bed gasification (FBG) pilot plant was designed and constructed at the University of Saskatchewan Chemical Engineering Department Fluidization Laboratory. FBG is a thermo-chemical method for converting solid biomass to a gaseous fuel, termed syngas. Several instrumentation and control issues were particularly challenging with this pilot plant, including development of the fuel feeding system, pressure measurement of high temperature fluids, and metering of steam as a process reactant.<p> The fuel feeding system was tested using MBM (meat and bone meal) to determine the output rate stability, and predictability and measurability of the system as the components in the fuel feeding system were integrated. The fuel feeding system that was tested included a 150 mm primary metering screw conveyor, a 150 mm rotary airlock, and a 50 mm secondary injection screw conveyor. Each component of the system was fitted with a 3-phase electric motor and a variable speed drive to allow for a variable output rate. The weighing system that was integral to the metering conveyor was tested as well, but upon pressurizing the metering conveyor and hopper, the weighing system sustained an unreasonable amount of noise. Integrating a pneumatic injection nozzle with the injection conveyor was found to work effectively both under ambient temperatures and hot FBG conditions up to 725oC. Above 725oC, it was found that the test fuel would char and coat the nozzle, causing it to plug. Testing of the feeding system with the injection nozzle removed illustrated that the system could work well without it. It was determined that the injection conveyor speed to metering conveyor speed ratio that should be used for this system was 1:110 for absolute rotational speeds, or 1:1 of the full conveyor speeds. The complete system, including the injection nozzle, was analyzed and determined to produce a fuel output rate (FS) for % speeds from 5-25%, which roughly corresponded to the desired plant fuel feed rate of 1-5 g/s.<p> Techniques for remote pressure measurement of fluidized beds were examined as well, including the use of long tubes to cool hot gases and filters for blocking solid particles. The pressure measurement delay of these techniques was examined in comparison to a direct local measurement. This was conducted by comparing the pressure readings from two identical sensors; one mounted directly to a manifold, and the other mounted via a variable assembly (comprised of a variable length of 6.35 mm (1/4") PE tubing and a porous plate filter). Assemblies without a porous plate were found to have a minimal delay of up to 0.303 seconds for 30 m length of PE impulse tubing. More significant delays were found for systems using both a 10 media grade porous plate filter and impulse tubing; a 3 m tube length with filter has a delay of up to 0.221 s, and a 30 m impulse tube combined with the filter has a measurement delay of up to 1.915 s, a significant delay in cases where high-frequency analysis of pressure is used for bed agglomeration prediction, or systems where fast response is required to changing pressure conditions.<p> Additionally, a steam flow measurement system using an orifice plate and differential pressure sensor was installed and calibrated. By collecting time-based steam samples and process data, the physical system coefficients were determined for this system, allowing for steam flow measurement, accurate within 5% over a flow range of 0.5 to 2.0 g/s.

A fluidized bed gasification (FBG) pilot plant was designed and constructed at the University of Saskatchewan Chemical Engineering Department Fluidization Laboratory. FBG is a thermo-chemical method for converting solid biomass to a gaseous fuel, termed syngas. Several instrumentation and control issues were particularly challenging with this pilot plant, including development of the fuel feeding system, pressure measurement of high temperature fluids, and metering of steam as a process reactant.<p> The fuel feeding system was tested using MBM (meat and bone meal) to determine the output rate stability, and predictability and measurability of the system as the components in the fuel feeding system were integrated. The fuel feeding system that was tested included a 150 mm primary metering screw conveyor, a 150 mm rotary airlock, and a 50 mm secondary injection screw conveyor. Each component of the system was fitted with a 3-phase electric motor and a variable speed drive to allow for a variable output rate. The weighing system that was integral to the metering conveyor was tested as well, but upon pressurizing the metering conveyor and hopper, the weighing system sustained an unreasonable amount of noise. Integrating a pneumatic injection nozzle with the injection conveyor was found to work effectively both under ambient temperatures and hot FBG conditions up to 725oC. Above 725oC, it was found that the test fuel would char and coat the nozzle, causing it to plug. Testing of the feeding system with the injection nozzle removed illustrated that the system could work well without it. It was determined that the injection conveyor speed to metering conveyor speed ratio that should be used for this system was 1:110 for absolute rotational speeds, or 1:1 of the full conveyor speeds. The complete system, including the injection nozzle, was analyzed and determined to produce a fuel output rate (FS) for % speeds from 5-25%, which roughly corresponded to the desired plant fuel feed rate of 1-5 g/s.<p> Techniques for remote pressure measurement of fluidized beds were examined as well, including the use of long tubes to cool hot gases and filters for blocking solid particles. The pressure measurement delay of these techniques was examined in comparison to a direct local measurement. This was conducted by comparing the pressure readings from two identical sensors; one mounted directly to a manifold, and the other mounted via a variable assembly (comprised of a variable length of 6.35 mm (1/4") PE tubing and a porous plate filter). Assemblies without a porous plate were found to have a minimal delay of up to 0.303 seconds for 30 m length of PE impulse tubing. More significant delays were found for systems using both a 10 media grade porous plate filter and impulse tubing; a 3 m tube length with filter has a delay of up to 0.221 s, and a 30 m impulse tube combined with the filter has a measurement delay of up to 1.915 s, a significant delay in cases where high-frequency analysis of pressure is used for bed agglomeration prediction, or systems where fast response is required to changing pressure conditions.<p> Additionally, a steam flow measurement system using an orifice plate and differential pressure sensor was installed and calibrated. By collecting time-based steam samples and process data, the physical system coefficients were determined for this system, allowing for steam flow measurement, accurate within 5% over a flow range of 0.5 to 2.0 g/s.

Process Automation Systems’ design, selection, planning and implementation play a contributing role in achieving success in Industrial Megaprojects within the Oil and Gas Industry. Process Automation Systems represent only 8% - 10% of the total installed cost in capital projects, but the reliability and performance of Process Automation Systems are fundamental factors to ensure the operability and safety of new plants within the oil and gas industry. Recent studies show an increasing number of Industrial Megaprojects in execution during the last decade, a better understanding of the real impact that these projects can bring to our societies, the complexity of these endeavors and the likelihood of having more megaprojects being approved during the next 20 years in the global market. It is pleasant to hear that there are favorable conditions present in the industry to promote and execute capital projects, but there is an alarming rate at which these capital projects overrun schedules and budgets. Project execution key performance indicators such as cost growth, cost index, schedule index, schedule slippage and operability index often applied to measure the success of Megaprojects, should be carefully followed by project management teams, during the implementation of Process Automation Systems. In the oil and gas industry megaprojects are executed in a stage gated work process typically divided into phases with a pause for assessment and decision about whether to proceed. The gate assessments examine both economic/business and technical aspects of the project, to make decisions to stop, recycle or proceed. The purpose of this research is to identify practices in a stage gated work process approach (FEL Front End Loading) to increase the probability of success in Process Automation Systems implementation given complexity factors in Industrial Megaprojects. / text

Currently process industry faces a paradoxical situation. On the one hand there is the urgent need to optimise the performance of processes by increasing throughput, decreasing operating costs while increasing the product quality. On the other hand there are only few specialists in industry who are able to develop and apply appropriate control strategies for the increasingly complex processes in the process industry. Generally, these specialists work in research and development departments necessitating a considerable amount of time to develop sophisticated solutions for specific processes. However, in the process industry control design and fine-tuning are mostly done by practitioners more than by specialists, directly at the process and in a minimum of time. Within this commissioning phase the process is assembled and set into operation, often with suboptimally turned controllers. Efforts have been undertaken to support these commissioners doing their tasks, and for single variable processes practically applicable methods have been developed. Nevertheless, for more complex processes the generation of mathematical process models as an appropriate base for control system design still is a major problem in practice. The subject of this work is the development of a structured approach to identification techniques for the analysis of industrial processes that enables industrial users with limited control engineering knowledge to design process models suitable for the design of industrial controllers. This latter aspect has been addressed within the collaborative research project between the University of Glamorgan and the Fachhochschule Hannover, of which the work presented in this thesis is a substantial part. Therefore, an industrially suitable scheme for computer aided control system design (CACSD) has firstly been developed in agreement with industrial users in order to set the frame for the research project. This scheme has been based on simple block-oriented model structures composed from nonlinear static and linear dynamic characteristics. The scheme is simple in use and intuitive to understand and follow. Therefore, it can be directly applied also by inexperienced engineers, who look for quick and efficient solutions as a basis even for nonlinear controller design. Beyond this a standardised identification procedure for nonlinear processes has been elaborated in order to provide process models fitting to the CACSD scheme. This standardised identification procedure has been equipped with two improved algorithms. For the approximation of even multidimensional static characteristics a capable method has been developed necessitating neither apriori information nor user interaction. For the identification of discrete-time linear dynamic models a two-step identification method has been improved by a numerically efficient least squares estimator that allows the parallel estimation of a set of model structures, which is evaluated automatically. For the validation of the proposed approach and the developed methods a prototype identification tool has been programmed, which also lays the ground for the integration of the whole CACSD scheme into a block-oriented simulation environment.

This paper summarizes a panel discussion at the 15th International Conference on Business Process Management. The panel discussed to what extent the emergence of recent technologies including machine learning, robotic process automation, and blockchain will reduce the human factor in business process management. The panel discussion took place on 14 September, 2017, at the Universitat Politècnica de Catalunya in Barcelona, Spain. Jan Mendling served as a chair; Gero Decker, Richard Hull, Hajo Reijers, and Ingo Weber participated as panelists. The discussions emphasized the impact of emerging technologies at the task level and the coordination level. The major challenges that the panel identified relate to employment, technology acceptance, ethics, customer experience, job design, social integration, and regulation.

This paper summarizes a panel discussion at the 15th International Conference on Business Process Management. The panel discussed to what extent the emergence of recent technologies including machine learning, robotic process automation, and blockchain will reduce the human factor in business process management. The panel discussion took place on 14 September, 2017, at the Universitat Politècnica de Catalunya in Barcelona, Spain. Jan Mendling served as a chair; Gero Decker, Richard Hull, Hajo Reijers, and Ingo Weber participated as panelists. The discussions emphasized the impact of emerging technologies at the task level and the coordination level. The major challenges that the panel identified relate to employment, technology acceptance, ethics, customer experience, job design, social integration, and regulation.

Robotic Process Automation (RPA) is an automation technology that has become increasingly common in recent years, as demands have increased on the use of new technologies to compete in the global economy. RPA means that a software robot is placed on top of existing systems where it performs tasks in a similar way as a human being previously did. No changes need to be made to the underlying systems, which means that RPA is rather quick and easy to implement at a relatively low cost. There are many advantages to the usage of RPA, but not all processes are suitable for the implementation of robots. The purpose of the study has been to help businesses select the parts of the organization that can be automated by identifying criteria which means that RPA can be applied to a process. In order to fulfill the purpose of the study, information has been collected through a literature study and semi-structured interviews with consultants working in the RPA area. The result shows that there are several criteria that increase the suitability of an RPA implementation on a process, but also that the importance of different criteria varies from situation to situation. The study has also identified criteria that must be met in order to be able to implement robots at all. Since the appearance of different companies and processes varies greatly, the criteria should be seen as a guideline where each situation is examined individually to identify what is important right there. Initially, a detailed analysis needs to be done of a candidate process in order to determine the degree of suitability and whether RPA is a good solution in that case. / Robotic Process Automation (RPA) är en automatiseringsteknik som blivit allt vanligare de senaste åren. Kraven har ökat på användandet av nya teknologier för att kunna konkurrera i den globala ekonomin. RPA innebär att en mjukvarurobot placeras ovanpå befintliga system där den utför uppgifter på ett likadant sätt som en människa tidigare gjorde. Inga ändringar behöver göras på de underliggande systemen vilket medför att RPA är förhållandevis snabbt och enkelt att införa med relativt låg kostnad. Det finns många fördelar med användningen av RPA, men alla processer är inte lämpliga att implementera robotar på. Studiens syfte har varit att hjälpa verksamheter att välja ut de delar i organisationen som kan automatiseras genom att identifiera kriterier som medför att RPA går att använda på en process. För att uppfylla studiens syfte har information samlats in via en litteraturöversikt och semistrukturerade intervjuer med konsulter som arbetar inom RPA-området. Resultatet visar att det finns ett flertal kriterier som ökar lämplighetsgraden för införandet av RPA på en process, men också att vikten av olika kriterier varierar från situation till situation. Studien har även identifierat kriterier som måste uppfyllas för att det överhuvudtaget ska gå att implementera robotar. Eftersom utseendet på olika företag och processer varierar kraftigt så bör kriterierna ses som en riktlinje där varje situation utreds individuellt för att identifiera vad som är viktigt just där. Initialt behöver en utförlig analys göras av en kandidatprocess för att kunna fastställa lämplighetsgraden och om RPA är en bra lösning i det fallet.

Robotic process automation (RPA) är en växande trend inom alla möjliga arbetsområden. En RPA-mjukvara har kapaciteten att utföra enformiga digitala uppgifter på ett nästintill mänskligt sätt tack vare dess förmåga att jobba mot alla typer av system och gränssnitt. Potentialen är enorm, däribland för övervakning och datautvinning. Syftet med detta projekt var att utforska RPA:ns möjligheter gällande övervakning, samt dess framtida potential. För det byggdes en så kallad RPA-robot i utvecklingsmiljön UiPath Studio för att automatisera processen att hämta väderinformation från tre olika väderwebbsidor, och testa robotens styrkor och svagheter. Resultatet blev en färdig robot som uppnådde kraven som ställts på den, samt visade det på en bra prestanda vid typiska förutsättningar där en körning genomsnittligen låg på under en minut. Det visade dock också att RPA har tydliga brister, som vid förändringar av struktur och diverse störningar, och det visade att implementation spelar väldigt stor roll i hur roboten hanterar diverse situationer. Det som härledes från resultatet var att RPA är en något stel men samtidigt otroligt användbar teknologi som är bra för det den är ämnad för. Den har stor potential, särskilt i kombination med andra teknologier som artificiell intelligens, som kan täcka RPA:ns begränsningar, och ämnar sig utmärkt till alla typer av processer. / Robotic process automation (RPA) is a growing trend withing all fields of work. An RPA-software has the capacity to perform monotonous digital tasks in a nearly human manner thanks to its ability to work across all systems and interfaces. The potential is huge, for surveillance and data mining among all other areas. The purpose of this project was to explore the possibilities of RPA regarding surveillance, as well as its future potential. For that, a so-called RPA-robot was built in the tool UiPath Studio to automate the process of fetching weather information from three different weather websites, and to test the strengths and weaknesses of the robot. The result was a finished robot which reached the established requirements and showed good performance under typical circumstances, where one run of the robot on average took under one minute. However, it also showed that RPA has clear flaws, such as when structure changes and when affected by various disturbances. What can be deduced from the result was that RPA is a somewhat stiff but also incredibly useful technology which is good for what it is meant for. It has great potential, especially in combination with other technologies like artificial intelligence, which can fill the gaps of RPA’s limitations, and it works well for all types of processes, among them data mining and surveillance.