16 results found
Kusumo K, Kuriyan K, Vaidyaraman S, et al., 2022, Probabilistic framework for optimal experimental campaigns in the presence of operational constraints, Reaction Chemistry and Engineering, Vol: 7, Pages: 2359-2374, ISSN: 2058-9883
The predictive capability of any mathematical model is intertwined with the quality of experimentaldata collected for its calibration. Model-based design of experiments helps compute maximallyinformative campaigns for model calibration. But in early stages of model development it is crucial toaccount for model uncertainties to mitigate the risk of uninformative or infeasible experiments. Thisarticle presents a new method to design optimal experimental campaigns subject to hard constraintsunder uncertainty, alongside a tractable computational framework. This computational frameworkinvolves two stages, whereby the feasible experimental space is sampled using a probabilistic approachin the first stage, and a continuous-effort optimal experiment design is determined by searching overthe sampled feasible space in the second stage. The tractability of this methodology is demonstratedon a case study involving the exothermic esterification of priopionic anhydride with significant risk ofthermal runaway during experimentation. An implementation is made freely available based on thePython packages DEUS and Pydex.
Kusumo K, Kuriyan K, Vaidyaraman S, et al., 2022, Risk mitigation in model-based experiment design: a continuous-effort approach to optimal campaigns, Computers and Chemical Engineering, Vol: 159, ISSN: 0098-1354
A key challenge in maximizing the effectiveness of model-based design of experiments for calibrating nonlinear process models is the inaccurate prediction of information that is afforded by each new experiment. We present a novel methodology to exploit prior probability distributions of model parameter estimates in a bi-objective optimization formulation, where a conditional-value-at-risk criterion is considered alongside an average information criterion. We implement a tractable numerical approach that discretizes the experimental design space and leverages the concept of continuous-effort experimental designs in a convex optimization formulation. We demonstrate effectiveness and tractability through three case studies, including the design of dynamic experiments. In one case, the Pareto frontier comprises experimental campaigns that significantly increase the information content in the worst-case scenarios. In another case, the same campaign is proven to be optimal irrespective of the risk attitude. An open-source implementation of the methodology is made available in the Python software Pydex.
Kusumo KP, Kuriyan K, García-Muñoz S, et al., 2021, Continuous-Effort Approach to Model-Based Experimental Designs, Computer Aided Chemical Engineering, Pages: 867-873
Model-based design of experiments is a technique for accelerating the development of mathematical models. Through maximally informative experiments, time and resources for estimating uncertain model parameters are minimized. This article presents a method for computing effort-based experimental designs, whereby designs are akin to experimental recipes. As well as identifying which experiments are the most informative, the optimal experimental effort to dedicate to each experiment is also optimized. Upon discretizing the experimental design space and treating the efforts as continuous decision variables, this method leads to convex optimization problems regardless of the model structure, which is ideal for large, parallel experimental campaigns. The case study of a batch reactor model with four parameters is presented to illustrate the methodology.
Aunedi M, Pantaleo AM, Kuriyan K, et al., 2020, Modelling of national and local interactions between heat and electricity networks in low-carbon energy systems, Applied Energy, Vol: 276, Pages: 1-18, ISSN: 0306-2619
Decarbonisation of the heating and cooling sector is critical for achieving long-term energy and climate change objectives. Closer integration between heating/cooling and electricity systems can provide additional flexibility required to support the integration of variable renewables and other low-carbon energy sources. This paper proposes a framework for identifying cost-efficient solutions for supplying district heating systems within both operation and investment timescales, while considering local and national-level interactions between heat and electricity infrastructures. The proposed optimisation model minimises the levelised cost of a portfolio of heating technologies, and in particular Combined Heat and Power (CHP) and polygeneration systems, centralised heat pumps (HPs), centralised boilers and thermal energy storage (TES). A number of illustrative case studies are presented, quantifying the impact of renewable penetration, electricity price volatility, local grid constraints and local emission targets on optimal planning and operation of heat production assets. The sensitivity analysis demonstrates that the cost-optimal TES capacity could increase by 41–134% in order to manage a constraint in the local electricity grid, while in systems with higher RES penetration reflected in higher electricity price volatility it may be optimal to increase the TES capacity by 50–66% compared to constant prices, allowing centralised electric HP technologies to divert excess electricity produced by intermittent renewable generators to the heating sector. This confirms the importance of reflecting the whole-system value of heating technologies in the underlying cost-benefit analysis of heat networks.
Jing R, Kuriyan K, Lin J, et al., 2020, Quantifying the contribution of individual technologies in integrated urban energy systems – A system value approach, Applied Energy, Vol: 266, ISSN: 0306-2619
Integrated urban energy systems satisfy energy demands in a cost-effective manner by efficiently combining diverse technologies and energy saving strategies. However, the contribution of an individual technology within a complex system is difficult to quantify. This study introduces a generalized “system value” approach to quantify the contribution of an individual design decision towards improving the system design (e.g., achieving a lower cost design). It measures the contribution of an individual technology to the whole system in the range between two benchmarks that respectively represent complete exclusion of the technology and the optimal penetration level. The method is based on a technology-rich Mixed Integer Linear Programming (MILP) model for optimal design of urban energy systems. The model considers multi-energy supply technologies, networks, storage technologies and various energy saving strategies. A stochastic formulation is further developed to quantify uncertainties of the system value. The system values of nine kinds of energy supply technologies and three categories of energy-saving strategies are quantified via a case study, which illustrates the variation in the system values for individual technologies with different levels of penetration, and multi-energy supply technologies can have a large impact in integrated systems.
Kong Q, Kuriyan K, Shah N, et al., 2019, Development of a responsive optimisation framework for decision-making in precision agriculture, Computers and Chemical Engineering, Vol: 131, ISSN: 0098-1354
Emerging digital technologies and data advances (e.g. smart machinery, remote sensing) not only enable Agriculture 4.0 to envisage interconnected agro-ecosystems and precision agriculture but also demand responsive decision-making. This study presents a mathematical optimisation model to bring real-time data and information to precision decision-support and to optimise short-term farming operation. To achieve responsive decision-support, we proposed two meta-heuristic algorithms i.e. a tailored genetic algorithm and a hybrid genetic-tabu search algorithm for solving the deterministic optimisation. The developed responsive optimisation framework has been applied to a hypothetical case study to optimise sugarcane harvesting in the KwaZulu Natal region in South Africa. In comparison with the optimal solutions derived from the exact algorithm, the proposed meta-heuristic methods lead to near optimal solutions (less than 5% from optimality) and significantly reduced computational time by over 95%. Our results suggest that the tailored genetic algorithm enables rapid solution searching but the solution quality on sugarcane harvesting cannot compete with the exact method. The hybrid genetic-tabu search algorithm achieved a good trade-off between computational time reduction and solution optimality, demonstrating the potential to enhance responsive decision making in precision sugarcane farming. Our research highlights the development of the responsive optimisation framework combining mixed integer linear programming and hybrid meta-heuristic search algorithms and its applications in real-time decision-making under Agriculture 4.0 vision.
Jing R, Kuriyan K, Kong Q, et al., 2019, Exploring the impact space of different technologies using a portfolio constraint based approach for multi-objective optimization of integrated urban energy systems, Renewable and Sustainable Energy Reviews, Vol: 113, Pages: 1-12, ISSN: 1364-0321
Optimization-based modelling provides valuable guidance for designing integrated urban energy systems. However, modelers have to make certain assumptions and they may lack awareness of realistic conditions such as decision-makers’ preferences on certain technology, which can easily lead the obtained optimal solution to be invalid. Therefore, instead of focusing on one “fragile” optimal solution, this paper provides a systematic overview of the contribution each technology can bring to the whole system design so as to achieve the optimum. To achieve this, a portfolio constraint based approach is proposed, which is inspired by the modelling to generate alternatives (MGA) method as well as the eps-constraint method for multi-objective optimization. By varying the threshold values of portfolio constraints, a series of solutions can be gathered as an “impact space” representing the economic and environmental contributions of each technology for the whole system design. A practical Fitting of Ellipses method is further applied to quantify the size of the impact space. Through observing the formation of the impact space, more valuable insights on system design can be obtained. The proposed approach is applied to a case study of an urban district in Shanghai, China, where a generalized urban energy system model involving commonly used energy supply technologies is established. Various technologies and design options lead to significantly different impact spaces, where CHP is found to have the largest impact on system design. Overall, instead of merely providing decision-maker a very specific solution, this paper introduces a new approach to evaluate multiple technologies when designing integrated urban energy systems.
Aunedi M, Kuriyan K, Pantaleo AM, et al., 2019, Multi-scale modelling of interactions between heat and electricity networks in low-carbon energy systems, 14th Conference on Sustainable Development of Energy, Water and Environment Systems – SDEWES Conference, Publisher: SDEWES
Decarbonisation of the heating and cooling sector is critical for achieving long-term energy and climate change objectives. Closer integration between heating/cooling and electricity systems can provide additional flexibility required to support the integration of variable renewables and other low-carbon energy sources. This paper proposes a framework for identifying cost-efficient solutions for supplying district heating systems within both operation and investment timescales, while considering local and national-level interactions between heat and electricity infrastructures. The proposed approach cost-optimises the portfolio of heating technologies, including Combined Heat and Power (CHP) and polygeneration systems, large-scale heat pumps (HPs), gas boilers and thermal energy storage (TES). It is implemented as a mixed-integer linear programming (MILP) optimisation model that minimises net cost of heat supply, taking into account investment and operation cost of heat supply and storage options as well as the impact of local and wider interactions with the electricity system.
Kuriyan K, Shah N, 2019, A combined spatial and technological model for the planning of district energy systems, International Journal of Sustainable Energy Planning and Management, Vol: 21, Pages: 111-131, ISSN: 2246-2929
This paper describes a combined spatial and technological model for planning district energy systems. The model is formulated as a mixed integer linear program (MILP) and selects the optimal mix of technology types, sizes and fuels for local energy generation, combined with energy imports and exports. The model can also be used to select the locations for the energy sources, the distribution route, and optionally, to select the heat loads that will be connected to a district energy system. The optimisation model combines a map-based spatial framework, describing the potential distribution network structure, with a flexible Resource Technology Network (RTN) representation which incorporates multiple heat sources. Results for scenarios based on a test dataset are presented and show the impact of heat prices on the designed network length. The results illustrate the use of Combined Heat and Power (CHP) units to satisfy internal and external power demands, and also demonstrate their use in combination with heat pumps to satisfy emissions targets. A system value metric is introduced to quantify the incremental impact of investments in the heat network in areas of varying heat density. A procedure for screening potential supply locations to reduce computational requirements is proposed.
Kuriyan K, Shah N, 2017, Trade-offs in the Design of Urban Energy Systems, Editors: Espuna, Graells, Puigjaner, Publisher: ELSEVIER SCIENCE BV, Pages: 2383-2388
Kuriyan K, Shah N, 2016, Tools and Workflows in the Design of Urban Energy Systems, American Institue of Chemical Engineers, Annual Meeting
Kuriyan K, 2015, An Interactive Framework for Building and Analysing Models of Urban Energy Systems, Pages: 971-976, ISSN: 1570-7946
This paper describes a software framework for building and analysing models of urban energy systems. The framework consists of a technology database and components for model assembly and networking coupled with a graphical user interface. The technology database, which is implemented as a Protégé ontology, describes the available energy conversion, storage and transportation processes. The spatial framework for the model is defined by a cityspace object. Distributed energy demands are specified within this cityspace, and it also identifies potential locations for energy conversion processes and links for resource flows across infrastructure networks. The user interface instantiates and assembles framework objects into an overall object model of the system. The assembled object model can be used to define optimization scenarios for the design of urban energy systems with specific goals and constraints such as minimising investment costs while meeting emission targets. These scenarios can be submitted across a network to a solver on a remote host to obtain an optimal design of the urban energy system. The results are displayed by the user interface at two levels: an aggregate level with key performance indicators and graphical analysis for the city as a whole, and a detailed level showing information about individual cells and flows between cells. The interface also provides charts and summaries for comparing alternative scenarios with variations in emission targets, cost parameters and available technologies.
Kuriyan K, Reklaitis GV, 2013, Creating shared resources for pharmaceutical technology education-Simulation tools, Education for Chemical Engineers, Vol: 8, ISSN: 1749-7728
This article describes the use of the HUBZero middleware to create shared simulations of particulate processes in drug product manufacturing. These simulations are deployed on the pharmaHUB.org Web-site and are accessible through a Web-browser. The simulations may be used to introduce basic concepts in solids processing and their application to drug product design and process development. Graphical interfaces that allow students to visualize the models being studied may be constructed with the Rappture toolkit. This toolkit generates an interface from a simple input/output description that is applicable to many simple but useful engineering models. The toolkit allows contributors to convert existing simulation programs, which typically rely on text based input and output, into Web-based tools with graphical interfaces. As an example, the article presents a simulation tool that models roller compaction, a unit operation used in dry granulation processes for drug product manufacturing. The article also reviews instructional frameworks for integrating simulation tools and other shared resources into courses. Supporting materials that provide a more detailed description of roller compaction are also identified. © 2013 The Institution of Chemical Engineers.
Kuriyan K, Catlin AC, Reklaitis GV, 2009, pharmaHUB: Building a virtual organization for pharmaceutical engineering and science, Journal of Pharmaceutical Innovation, Vol: 4, Pages: 81-89, ISSN: 1872-5120
Information technology and high-performance computing play a critical role in the development of virtual organizations for science and engineering disciplines. In this paper, we report on our efforts to design and develop pharmaHUB.org, a prototype virtual organization that intends to promote the joint creation and sharing of tools, knowledge, and educational materials across the various science and engineering disciplines related to pharmaceutical product development and manufacturing. pharmaHUB has been brought online using middleware from HUBzero which provides content management and community building capabilities and also makes it possible to deliver simulation tools through the web. By leveraging the HUBzero middleware and forming partnerships with existing communities of researchers, it has been possible to rapidly develop a prototype virtual organization with a significant offering of educational and simulation tool resources. Prototype tools to support the knowledge management needs of pharmaceutical development have also been integrated into the hub environment. © International Society for Pharmaceutical Engineering 2009.
Hlinak AJ, Kuriyan K, Morris KR, et al., 2006, Understanding critical material properties for solid dosage form design, Journal of Pharmaceutical Innovation, Vol: 1, Pages: 12-17, ISSN: 1872-5120
What is the role of standardized methods for determining the impact of material properties in pharmaceutical formulation and process development? In this Perspective article, we identify material properties that are potentially important in solid dosage form design, and we review approaches linking these properties to product specifications in dry granulation process development. We also assess the potential benefits that could be obtained by standardizing the methods for determining the impact of material properties of commonly used excipients and propose a program of research to achieve the desired goal of an efficient, science-based approach for incorporating material properties in solid dosage form design. © 2006 ISPE. All rights reserved.
Kuriyan K, Muench W, Reklaitis GV, 2001, Air Products Hydrogen Liquefaction Project: Building a Web-Based Simulation of an Industrial Process, Computer Applications in Engineering Education, Vol: 9, Pages: 180-191, ISSN: 1061-3773
In this paper we present a Web-based simulation drawn from a hydrogen liquefaction process operated by Air Products. We describe the software architecture that connects Web-based clients to a simulation server and discuss some implementation issues. © 2001 John Wiley & Sons, Inc.
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