Techno-economic assessment

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Techno-economic assessment or techno-economic analysis (abbreviated TEA) is a method of analyzing the economic performance of an industrial process, product, or service. The methodology originates from earlier work on combining technical, economic and risk assessments for chemical production processes [1] . It typically uses software modeling to estimate capital cost, operating cost, and revenue based on technical and financial input parameters. [2] One desired outcome is to summarize results in a concise and visually coherent form, using visualization tools such as tornado diagrams and sensitivity analysis graphs.

Contents

At present, TEA is most commonly used to analyze technologies in the chemical, bioprocess, petroleum, energy, and similar industries. This article focuses on these areas of application.

Use cases

TEA can be used for studying new technologies or optimizing existing ones. Ideally, a techno-economic model represents the best current understanding of the system being modeled. The following are examples of typical uses.

Methodology

Techno-economic analyses are usually performed using a techno-economic model. A techno-economic model is an integrated process and cost model. It combines elements of process design, process modeling, equipment sizing, capital cost estimation, and operating cost estimation.

Process design

To begin with, the system is defined in the form of a process flow diagram (PFD). A typical PFD shows major equipment and material streams. The term ‘material stream’ refers to liquids, solids, or gases entering or exiting the system, or flowing from one piece of equipment to another.

Process modeling

The process model uses engineering and material balance calculations to more fully characterize the system being analyzed. The results are often summarized in the form of a material balance table or stream table, which corresponds to the PFD.

Equipment sizing

The output from the process model is used to:

  1. Estimate sizing parameters for each piece of equipment (i.e. one or more parameters that correlate with cost)
  2. Estimate utility requirements for each piece of equipment (i.e. electrical power, fuel, cooling water, etc.)

Capital cost estimation

Capital costs are typically estimated using a major equipment factored approach. [3] [4] First, the purchase cost for each piece of equipment is estimated from the results of the equipment sizing calculations, often using power law scaling relationships. [2] Next, the balance of the capital costs are estimated by applying multiplying factors based on similar systems. [5]

Operating cost estimation

Typical operating costs include raw materials, operating labor, waste treatment, and disposal, utilities, and overhead. Raw material and waste treatment costs are estimated by applying prices to raw material and waste flow rates from the process model. Similarly, utility costs are estimated by applying prices to the utility rates from equipment sizing. [5]

Operating labor can be estimated based on equipment size, quantity, and type. Overhead is typically estimated by applying heuristic factors to capital costs and operating labor. [5]

Cash flow analysis

Techno-economic models may also include a discounted cash flow analysis to calculate metrics like net present value and internal rate of return. A cash flow analysis will typically incorporate financial parameters like taxes and discount rates.


Platforms

TEA is typically performed using one of two platforms: spreadsheet software, like Microsoft Excel, or a process simulator, like AVEVA Process Simulation, Aspen, SuperPro Designer, integrated tools such as thecubeSphere, or open source software such as the python-based BioSTEAM. [6] In general, these platforms use the methodology described above.

Spreadsheet modeling is often preferred for early-stage technologies and startups since it tends to offer greater flexibility, accessibility, and transparency. Process simulators, on the other hand, offer more powerful process simulation capabilities, greater standardization, and integrated cost-estimation modules.

More recently, researchers have demonstrated that machine learning models can be trained on simulation outputs to produce so-called surrogate models capable of predicting costs, mass balances, and energy balances. [7]

Accuracy

Assuming a complete process design, the major equipment factored approach that is often used in TEA has an expected accuracy of -30% to +50%. [4] In the early stages of development, however, the process design is often incomplete or inaccurate, so the error bounds are often considerably larger. Examples of how uncertainty is managed in process modeling and economic analysis of early stage technologies can be found for materials used in long duration energy storage and hydrogen storage. [8] [9]

Resources

Educational material

Online tools

Integrated tools

Guidelines

Related Research Articles

Process engineering is the understanding and application of the fundamental principles and laws of nature that allow humans to transform raw material and energy into products that are useful to society, at an industrial level. By taking advantage of the driving forces of nature such as pressure, temperature and concentration gradients, as well as the law of conservation of mass, process engineers can develop methods to synthesize and purify large quantities of desired chemical products. Process engineering focuses on the design, operation, control, optimization and intensification of chemical, physical, and biological processes. Process engineering encompasses a vast range of industries, such as agriculture, automotive, biotechnical, chemical, food, material development, mining, nuclear, petrochemical, pharmaceutical, and software development. The application of systematic computer-based methods to process engineering is "process systems engineering".

Industrial process control (IPC) or simply process control is a system used in modern manufacturing which uses the principles of control theory and physical industrial control systems to monitor, control and optimize continuous industrial production processes using control algorithms. This ensures that the industrial machines run smoothly and safely in factories and efficiently use energy to transform raw materials into high-quality finished products with reliable consistency while reducing energy waste and economic costs, something which could not be achieved purely by human manual control.

Design for Six Sigma (DFSS) is a collection of best-practices for the development of new products and processes. It is sometimes deployed as an engineering design process or business process management method. DFSS originated at General Electric to build on the success they had with traditional Six Sigma; but instead of process improvement, DFSS was made to target new product development. It is used in many industries, like finance, marketing, basic engineering, process industries, waste management, and electronics. It is based on the use of statistical tools like linear regression and enables empirical research similar to that performed in other fields, such as social science. While the tools and order used in Six Sigma require a process to be in place and functioning, DFSS has the objective of determining the needs of customers and the business, and driving those needs into the product solution so created. It is used for product or process design in contrast with process improvement. Measurement is the most important part of most Six Sigma or DFSS tools, but whereas in Six Sigma measurements are made from an existing process, DFSS focuses on gaining a deep insight into customer needs and using these to inform every design decision and trade-off.

<span class="mw-page-title-main">Biorefinery</span> Refinery that converts biomass to energy and other beneficial byproducts

A biorefinery is a refinery that converts biomass to energy and other beneficial byproducts. The International Energy Agency Bioenergy Task 42 defined biorefining as "the sustainable processing of biomass into a spectrum of bio-based products and bioenergy ". As refineries, biorefineries can provide multiple chemicals by fractioning an initial raw material (biomass) into multiple intermediates that can be further converted into value-added products. Each refining phase is also referred to as a "cascading phase". The use of biomass as feedstock can provide a benefit by reducing the impacts on the environment, as lower pollutants emissions and reduction in the emissions of hazard products. In addition, biorefineries are intended to achieve the following goals:

  1. Supply the current fuels and chemical building blocks
  2. Supply new building blocks for the production of novel materials with disruptive characteristics
  3. Creation of new jobs, including rural areas
  4. Valorization of waste
  5. Achieve the ultimate goal of reducing GHG emissions
<span class="mw-page-title-main">Computer-aided production engineering</span>

Computer-aided production engineering (CAPE) is a relatively new and significant branch of engineering. Global manufacturing has changed the environment in which goods are produced. Meanwhile, the rapid development of electronics and communication technologies has required design and manufacturing to keep pace.

<span class="mw-page-title-main">Pilot plant</span>

A pilot plant is a pre-commercial production system that employs new production technology and/or produces small volumes of new technology-based products, mainly for the purpose of learning about the new technology. The knowledge obtained is then used for design of full-scale production systems and commercial products, as well as for identification of further research objectives and support of investment decisions. Other (non-technical) purposes include gaining public support for new technologies and questioning government regulations. Pilot plant is a relative term in the sense that pilot plants are typically smaller than full-scale production plants, but are built in a range of sizes. Also, as pilot plants are intended for learning, they typically are more flexible, possibly at the expense of economy. Some pilot plants are built in laboratories using stock lab equipment, while others require substantial engineering efforts, cost millions of dollars, and are custom-assembled and fabricated from process equipment, instrumentation and piping. They can also be used to train personnel for a full-scale plant. Pilot plants tend to be smaller compared to demonstration plants.

In chemical engineering, process design is the choice and sequencing of units for desired physical and/or chemical transformation of materials. Process design is central to chemical engineering, and it can be considered to be the summit of that field, bringing together all of the field's components.

A process flow diagram (PFD) is a diagram commonly used in chemical and process engineering to indicate the general flow of plant processes and equipment. The PFD displays the relationship between major equipment of a plant facility and does not show minor details such as piping details and designations. Another commonly used term for a PFD is processflowsheet. It is the key document in process design.

A cost estimate is the approximation of the cost of a program, project, or operation. The cost estimate is the product of the cost estimating process. The cost estimate has a single total value and may have identifiable component values.

Material flow analysis (MFA), also referred to as substance flow analysis (SFA), is an analytical method to quantify flows and stocks of materials or substances in a well-defined system. MFA is an important tool to study the bio-physical aspects of human activity on different spatial and temporal scales. It is considered a core method of industrial ecology or anthropogenic, urban, social and industrial metabolism. MFA is used to study material, substance, or product flows across different industrial sectors or within ecosystems. MFA can also be applied to a single industrial installation, for example, for tracking nutrient flows through a waste water treatment plant. When combined with an assessment of the costs associated with material flows this business-oriented application of MFA is called material flow cost accounting. MFA is an important tool to study the circular economy and to devise material flow management. Since the 1990s, the number of publications related to material flow analysis has grown steadily. Peer-reviewed journals that publish MFA-related work include the Journal of Industrial Ecology, Ecological Economics, Environmental Science and Technology, and Resources, Conservation, and Recycling.

<span class="mw-page-title-main">Energy audit</span> Inspection, survey and analysis of energy flows in a building

An energy audit is an inspection survey and an analysis of energy flows for energy conservation in a building. It may include a process or system to reduce the amount of energy input into the system without negatively affecting the output. In commercial and industrial real estate, an energy audit is the first step in identifying opportunities to reduce energy expense and carbon footprint.

<span class="mw-page-title-main">Event chain methodology</span> Network analysis technique

Event chain methodology is a network analysis technique that is focused on identifying and managing events and relationships between them that affect project schedules. It is an uncertainty modeling schedule technique. Event chain methodology is an extension of quantitative project risk analysis with Monte Carlo simulations. It is the next advance beyond critical path method and critical chain project management. Event chain methodology tries to mitigate the effect of motivational and cognitive biases in estimating and scheduling. It improves accuracy of risk assessment and helps to generate more realistic risk adjusted project schedules.

<span class="mw-page-title-main">Material flow management</span>

Material flow management (MFM) is an economic focused method of analysis and reformation of goods production and subsequent waste through the lens of material flows, incorporating themes of sustainability and the theory of a circular economy. It is used in social, medical, and urban contexts. However, MFM has grown in the field of industrial ecology, combining both technical and economic approaches to minimize waste that impacts economic prosperity and the environment. It has been heavily utilized by the country of Germany, but it has been applied to the industries of various other countries. The material flow management process utilizes the Sankey Diagram, and echoes the circular economy model, while being represented in media environments as a business model which may help lower the costs of production and waste.

Process flowsheeting is the use of computer aids to perform steady-state heat and mass balancing, sizing and costing calculations for a chemical process. It is an essential and core component of process design.

<span class="mw-page-title-main">Process simulation</span>

Process simulation is used for the design, development, analysis, and optimization of technical process of simulation of processes such as: chemical plant s, chemical processes, environmental systems, power stations, complex manufacturing operations, biological processes, and similar technical functions.

Prospective Outlook on Long-term Energy Systems (POLES) is a world simulation model for the energy sector that runs on the Vensim software. It is a techno-economic model with endogenous projection of energy prices, a complete accounting of energy demand and supply of numerous energy vectors and associated technologies, and a carbon dioxide and other greenhouse gases emissions module.

Quantemol Ltd is based in University College London initiated by Professor Jonathan Tennyson FRS and Dr. Daniel Brown in 2004. The company initially developed a unique software tool, Quantemol-N, which provides full accessibility to the highly sophisticated UK molecular R-matrix codes, used to model electron polyatomic molecule interactions. Since then Quantemol has widened to further types of simulation, with plasmas and industrial plasma tools, in Quantemol-VT in 2013 and launched in 2016 a sustainable database Quantemol-DB, representing the chemical and radiative transport properties of a wide range of plasmas.

Analytica is a visual software developed by Lumina Decision Systems for creating, analyzing and communicating quantitative decision models. It combines hierarchical influence diagrams for visual creation and view of models, intelligent arrays for working with multidimensional data, Monte Carlo simulation for analyzing risk and uncertainty, and optimization, including linear and nonlinear programming. Its design is based on ideas from the field of decision analysis. As a computer language, it combines a declarative (non-procedural) structure for referential transparency, array abstraction, and automatic dependency maintenance for efficient sequencing of computation.

Aspen Plus, Aspen HYSYS, ChemCad and MATLAB, PRO are the commonly used process simulators for modeling, simulation and optimization of a distillation process in the chemical industries. Distillation is the technique of preferential separation of the more volatile components from the less volatile ones in a feed followed by condensation. The vapor produced is richer in the more volatile components. The distribution of the component in the two phase is governed by the vapour-liquid equilibrium relationship. In practice, distillation may be carried out by either two principal methods. The first method is based on the production of vapor boiling the liquid mixture to be separated and condensing the vapors without allowing any liquid to return to the still. There is no reflux. The second method is based on the return of part of the condensate to still under such conditions that this returning liquid is brought into intimate contact with the vapors on their way to condenser.

Sensitivity analysis identifies how uncertainties in input parameters affect important measures of building performance, such as cost, indoor thermal comfort, or CO2 emissions. Input parameters for buildings fall into roughly three categories:

References

  1. ↑ Bode, Gerald; Schomäcker, Reinhard; Hungerbühler, Konrad; McRae, Gregory J. (2007-11-07). "Dealing with Risk in Development Projects for Chemical Products and Processes". Industrial & Engineering Chemistry Research. 46 (23): 7758–7779. doi:10.1021/ie060826v. ISSN   0888-5885.
  2. 1 2 Chai, Slyvester Yew Wang; Phang, Frederick Jit Fook; Yeo, Lip Siang; Ngu, Lock Hei; How, Bing Shen (2022). "Future era of techno-economic analysis: Insights from review". Frontiers in Sustainability. 3. doi: 10.3389/frsus.2022.924047 . ISSN   2673-4524.
  3. ↑ Perry, Robert H.; Green, Don W. (2008). Perry's chemical engineers' handbook (8th ed.). New York: McGraw-Hill. pp. 9–10. ISBN   978-0-07-159313-7. OCLC   194071107.
  4. 1 2 AACE International (2005). Cost Estimate Classification System – As Applied In Engineering, Procurement, And Construction For The Process Industries; TCM Framework: 7.3 – Cost Estimating and Budgeting. Page 2.
  5. 1 2 3 Peters, Max S. (2003). Plant design and economics for chemical engineers. Klaus D. Timmerhaus, Ronald E. West (5th ed.). New York: McGraw-Hill. p. 273. ISBN   0-07-239266-5. OCLC   50410278.
  6. ↑ Cortes-Peña, Yoel; Kumar, Deepak; Singh, Vijay; Guest, Jeremy S. (2020-03-02). "BioSTEAM: A Fast and Flexible Platform for the Design, Simulation, and Techno-Economic Analysis of Biorefineries under Uncertainty". ACS Sustainable Chemistry & Engineering. 8 (8): 3302–3310. doi: 10.1021/acssuschemeng.9b07040 . ISSN   2168-0485.
  7. ↑ Huntington, Tyler; Baral, Nawa Raj; Yang, Minliang; Sundstrom, Eric; Scown, Corinne D. (1 February 2023). "Machine learning for surrogate process models of bioproduction pathways" (PDF). Bioresource Technology. 370: 128528. Bibcode:2023BiTec.37028528H. doi: 10.1016/j.biortech.2022.128528 . PMID   36574885. S2CID   255120419.
  8. ↑ Peng, Peng; Anastasopoulou, Aikaterini; Brooks, Kriston; Furukawa, Hiroyasu; Bowden, Mark; Long, Jeffrey; Autrey, Thomas; Breunig, Hanna (25 April 2022). "Cost and potential of metal–organic frameworks for hydrogen back-up power supply" (PDF). Nature Energy. 7 (5): 448–458. Bibcode:2022NatEn...7..448P. doi:10.1038/s41560-022-01013-w. S2CID   256708350.
  9. ↑ Peng, Peng; Yang, Lin; Menon, Akansha; Weger, Nathaniel; Prasher, Ravi; Prasher, Breunig; Hanna, Lubner; Sean (16 January 2022). "Techno-economic Analysis of High-Temperature Thermal Energy Storage for On-Demand Heat and Power". ChemRxiv. doi:10.26434/chemrxiv-2022-3l03r. S2CID   246032753.
  10. ↑ Zimmermann, Arno W.; Wunderlich, Johannes; Müller, Leonard; Buchner, Georg A.; Marxen, Annika; Michailos, Stavros; Armstrong, Katy; Naims, Henriette; McCord, Stephen; Styring, Peter; Sick, Volker (2020). "Techno-Economic Assessment Guidelines for CO2 Utilization" (PDF). Frontiers in Energy Research. 8. doi: 10.3389/fenrg.2020.00005 . ISSN   2296-598X.
  11. ↑ Scown, Corinne D; Baral, Nawa Raj; Yang, Minliang; Vora, Nemi; Huntington, Tyler (1 February 2021). "Technoeconomic analysis for biofuels and bioproducts" (PDF). Current Opinion in Biotechnology. 67: 58–64. doi: 10.1016/j.copbio.2021.01.002 . PMID   33477090. S2CID   231679223.
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