Main Key Performance Indicators Research Articles

HS3 as a novel solvent for carbon capture: Model validation and an industrial case study with comparison against 30 wt% MEA

CO2 capture is currently the most mature technological solution to reduce the environmental footprint of several emitters. However, the characterization of innovative blends with reduced energy demand and environmental impact is nowadays a challenge to increase the profitability and deployment of carbon capture on an industrial scale. This work investigates an innovative non-proprietary amine blend, called HS3. This article presents the development of a full model (including thermodynamics, kinetics, and mass transfer) and its validation with pilot-scale data covering temperature, CO2 concentration, and capture rate ranges of interest for industrial applications. The model predicts the main process Key Performance Indicators, such as CO2 captured, stripped flow, and cycling capacity, with deviations from the measurements lower than 7%. Then, the validated model is exploited for sizing and designing a carbon capture process from the flue gas generated within an oil refinery. A special focus is devoted to energy integration and process optimization by means of a sensitivity analysis. Eventually, HS3 performances are compared to benchmark MEA in terms of energy requirements and unit operation sizing for the same case study. Results show that HS3 can reduce the specific reboiler duty (MJ/kg CO2 captured) and the required solvent flow per unit of flue gas (kg/kg) by 21% and 19%, respectively. Examples of comprehensive models developed starting from lab-scale testing up to the validation on a pilot scale are still limited in the literature. The validation in a semi-industrial pilot is needed to fully understand the solvent properties including, for instance, drawbacks which cannot be detected on a small scale. Needless to mention, reliable models are needed for consistent scale-up, techno-economic assessment, and LCA analysis. Thus, this present presents a model validation using semi-industrial pilot data and measurements, which is not common in the literature.

A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation

The application of artificial intelligence (AI) techniques may lead to significant improvements in different aspects of rail sector. Considering asset management and maintenance, AI can improve data analysis and asset status forecasting and decision-making processes, fostering predictive and prescriptive maintenance strategies. A prescriptive approach should be able to predict future scenarios as well as to suggest a course of actions. Nevertheless, the decision-making in rail asset management is often based on the classical asset-oriented approach, concentrating on the function of the asset itself as a main key performance indicator (KPI), whereas a user-oriented approach could lead to improved performance in terms of level of service. This paper is aimed at integrating the passengers’ perspective in the decision-making process for asset management to mitigate the impact that service interruptions may have on the final users. A data-driven prioritisation framework is developed to prioritise maintenance interventions taking into account asset status and criticality. In particular, a three-step approach is proposed, which focuses on the analysis of passenger data to evaluate the failure impact on the service, the analysis of alarms and anomalies to evaluate the asset status, and the suggestion of maintenance interventions. The proposed approach is applied to the maintenance of the metro line M5 in the Italian city of Milan. Results show the usefulness of the proposed approach to support infrastructure managers and maintenance operators in making decisions regarding the priority of maintenance activities, reducing the risk of critical failures and service interruptions.

Assessing annual energy production using a combination of lidar and mast measurement campaigns

<span lang="EN-US">The current study evaluates the wind potential at early stage of wind project using met mast and lidar. The wind data obtained from a met mast located away from the turbines can be hindered by obstacles, leading to limited coverage and height, it negatively impacting the project's performances. To address the above limitations, the study suggests implementing a secondary measurement approach utilizing Lidars in various locations to cover the full study area. This work assesses the two measurements campaign and compares the wind potential evaluated with data measured by met mast and lidars. The simulations of two wind data base are quantified referring to the main key performance indicators for a project of total capacity of 140MW. The results show significant improvement in gross and net production for all exceedance scenarios, with 2% increase on the P90, and an improvement in the annual capacity factor by 1.09%. The use of Lidar data referenced to a met mast leads to accurate and bankable data, with the advances of cost and logistics, Lidars helps in layout configuration and performance evaluation.</span>

Company management based on key performance indicators: Industry overview

Key performance indicators (KPIs) act as a specific indicator of productivity, and reflect the degree of implementation of goals and objectives that have been set for specific employees, departments, or the organization as a whole. There are different key performance indicators depending on the scope of the organization. In any case, as a rule, indicators are divided into different groups to optimize the process of developing an organization’s strategic plan. For organizations, depending on the field of activity, the main KPIs and industry performance indicators are identified, the use of which is designed to increase the efficiency of activities, increase profits and motivation of employees. An important point in optimizing the company’s activities is the formation of an understanding among employees that the achievement of their goals depends on the achievement of the goals of the organization as a whole, since payments of additional financial incentives are made from the profit of the organization. Thus, KPI acts not only as a motivation system for employees, but also reflects the degree of achievement of goals by the business as a whole. Using the example of the gold mining industry, construction, transport and a number of other sectors of the national economy, the main key performance indicators are determined. It is concluded that in addition to the industry specifics, it is very important to take into account the specifics of a particular company when forming a KPI system.

Optimal Hysteresis Control via a Queuing System with Two Heterogeneous Energy-Consuming Servers

A queuing system having two different servers is under study. Demands enter the system according to a Markov arrival process. Service times have phase-type distribution. Service of demands is possible only if the fixed number of energy units, probably different for two servers, is available in the system at the potential service beginning moment. Energy units arrive in the system also according to a Markov arrival process and are stored in a stock (battery) of a finite capacity. Leakage of energy units from the stock can occur. Demands waiting in the infinite buffer are impatient and can leave the buffer after an exponentially distributed waiting time. One server is the main one and permanently provides service when the buffer is not empty and the required number of energy units is available. The second server is the assistant server and is switched on or off depending on the availability of energy units and queue length according to the hysteresis strategy defined by two thresholds. The assistant server is switched on when the queue length is not less than the greater threshold and is switched off when the queue length becomes smaller than the smaller threshold. The use of the assistant server has to be paid. Thus, the problem of the optimal selection of the thresholds defining the control strategy naturally arises. To solve this problem, the study of the behavior of the system under any fixed values of the parameters of the control strategy is necessary. Such a study is given in this paper. Numerical results are presented. They illustrate the feasibility of computer realization of the developed algorithms for computation of the stationary distribution of the system states and the main key performance indicators as well as the result of solving one of the possible optimization tasks.

Estimating electric power consumption of in-situ residential heat pump systems: A data-driven approach

International Energy Agency predicts that the global number of installed heat pumps (HP) will increase from 180 million in 2020 to approximately 600 million by 2030, covering 20% of buildings heating needs. Electric power consumption is one of the main key performance indicators for the heat pump systems from techno-economic perspective. However a common issue prevalent in many existing heat pumps is the lack of electric power measurement. The modern installations might be equipped with electric power measurement sensors but this comes at a higher system cost for the manufacturers and end-users. The primary objective of this work is to propose a virtual measurement for estimating power consumption, thereby eliminating the need for field measurement of power for heat pumps. To achieve the objective, a data-driven approach is proposed. Firstly, the in-situ data is preprocessed through data merging, cleaning, and normalization. Then, input features are pre-selected using Spearman correlation coefficients, and further refined by addressing multicollinearity problem. Following this, Extreme Gradient Boosting (XGBoost) models and polynomial models are developed by considering different features as inputs. All models are finally validated against the in-situ data from multi-units of ground source heat pump (GSHP) and air source heat pump (ASHP) installations. The results showed that the electric power consumption of GSHP can be estimated with high accuracy (99% for R2, 10 W for MAE, and 1% for MAPE) through generic data-driven models using only four easy-to-measure input features. Taking three input features as inputs for ASHP generic model, the accuracy can be reached to 83% for R2, 125 W for MAE, and 9% for MAPE. The method presented in this paper can be applied to estimate power consumption of millions of heat pumps and consequently add a significant value as well as provide different types of services, such as cost-saving benefits for manufacturers and end-users, flexibility services for aggregators and electricity grids.

Uncertainty Quantification for Thermodynamic Simulations with High-Dimensional Input Spaces Using Sparse Polynomial Chaos Expansion: Retrofit of a Large Thermal Power Plant

The assessment of the future thermodynamics performance of a retrofitted heat and power production unit is prone to many uncertainties due to the large number of parameters involved in the modeling of all its components. To carry out uncertainty quantification analysis, alternatives to the traditional Monte Carlo method must be used due to the large stochastic dimension of the problem. In this paper, sparse polynomial chaos expansion (SPCE) is applied to the retrofit of a large coal-fired power plant into a biomass-fired combined heat and power unit to quantify the main drivers and the overall uncertainty on the plant’s performance. The thermodynamic model encompasses over 180 components and 1500 parameters. A methodology combining the use of SPCE and expert judgment is proposed to narrow down the sources of uncertainty and deliver reliable probability distributions for the main key performance indicators (KPIs). The impact of the uncertainties on each input parameter vary with the considered KPI and its assessment through the computation of Sobol’ indices. For both coal and biomass operations, the most impactful input parameters are the composition of the fuel and its heating value. The uncertainty on the performance and steam quality parameters is not much affected by the retrofit. Key furnace parameters exhibit a skewed probability distribution with large uncertainties, which is a strong attention point in terms of boiler operation and maintenance.

Debaryomyces hansenii supplementation in low fish meal diets promotes growth, modulates microbiota and enhances intestinal condition in juvenile marine fish

BackgroundThe development of a sustainable business model with social acceptance, makes necessary to develop new strategies to guarantee the growth, health, and well-being of farmed animals. Debaryomyces hansenii is a yeast species that can be used as a probiotic in aquaculture due to its capacity to i) promote cell proliferation and differentiation, ii) have immunostimulatory effects, iii) modulate gut microbiota, and/or iv) enhance the digestive function. To provide inside into the effects of D. hansenii on juveniles of gilthead seabream (Sparus aurata) condition, we integrated the evaluation of the main key performance indicators coupled with the integrative analysis of the intestine condition, through histological and microbiota state, and its transcriptomic profiling.ResultsAfter 70 days of a nutritional trial in which a diet with low levels of fishmeal (7%) was supplemented with 1.1% of D. hansenii (17.2 × 105 CFU), an increase of ca. 12% in somatic growth was observed together with an improvement in feed conversion in fish fed a yeast-supplemented diet. In terms of intestinal condition, this probiotic modulated gut microbiota without affecting the intestine cell organization, whereas an increase in the staining intensity of mucins rich in carboxylated and weakly sulphated glycoconjugates coupled with changes in the affinity for certain lectins were noted in goblet cells. Changes in microbiota were characterized by the reduction in abundance of several groups of Proteobacteria, especially those characterized as opportunistic groups. The microarrays-based transcriptomic analysis found 232 differential expressed genes in the anterior-mid intestine of S. aurata, that were mostly related to metabolic, antioxidant, immune, and symbiotic processes.ConclusionsDietary administration of D. hansenii enhanced somatic growth and improved feed efficiency parameters, results that were coupled to an improvement of intestinal condition as histochemical and transcriptomic tools indicated. This probiotic yeast stimulated host-microbiota interactions without altering the intestinal cell organization nor generating dysbiosis, which demonstrated its safety as a feed additive. At the transcriptomic level, D. hansenii promoted metabolic pathways, mainly protein-related, sphingolipid, and thymidylate pathways, in addition to enhance antioxidant-related intestinal mechanisms, and to regulate sentinel immune processes, potentiating the defensive capacity meanwhile maintaining the homeostatic status of the intestine.

Perencanaan Dashboard Untuk Monitoring Kinerja Dosen Menggunakan Metode Noetix dan Rasmussen Pada Fakultas Ilmu Komputer Universitas Pamulang

Abstract
 
 The supervisory function of lecturers at the Faculty of Computer Science, Pamulang University is inseparable from teaching, research and community service activities. The increasing number of lecturers and the increasing complexity of the problems they face, demands organizational performance to be further improved. Monitored organizational performance is intended to ensure the achievement of the goals that have been set. Dashboard is a measurement instrument that provides Key Performance Indicator (KPI) information at a glance and in real-time.
 The dashboard is used as an instrument for monitoring performance, in supporting management needs at both the tactical and operational levels. KPI information is tailored to the needs of its users. The characteristic of this planned dashboard lies in the type of information displayed, as well as the way it is presented, even if only at a glance. Organizational KPI identification is obtained by mapping each KPI in the organization from the viewpoint of the displayed KPI priorities. The method used in designing the dashboard is the Noetix and Rasmussen method, the results of which are the main KPIs of the organization, the information hierarchy and the addition of alerts for the dashboard.
 
 Keywords: dashboard, monitoring, KPI, information hierarchy, alerts

Addressing sustainable energy intermittence for green ammonia production

This work aims to study the production of Green Ammonia (Green-NH3) from a plant solely powered with Renewable Energy (RE) sources. The main current challenge of green ammonia plants is dealing with the energy intermittency and unpredictability of RE sources while ensuring a steady state ammonia production to avoid damaging the non-flexible Haber Bosch (HB) catalytic reactor. This work delivers a novel Techno-Economic (TE) modeling approach aiming to optimize the design and operation of a pilot-scale G-NH3 plant. The proposed TE model takes into consideration the intermittency of the RE sources, by considering different site locations with consistent yearly meteorological data. Additionally, to ensure a continuous power supply to the HB reactor, the model uses an original Energy Management Strategy (EMS) to adequately dispatch power available from the RE sources to Charge/Discharge Energy Storage Systems (ESS). The TE performances of the plant were evaluated by considering two main Key Performance Indicators (KPIs): HB Load Factor (LF) and Levelized Cost of Ammonia (LCOA). The results of this study indicated that the implemented EMS allowed to efficiently reduce the fluctuation of the RE sources by adequately allocating the available power to different time slots, which consequently increased the HB LF by 56% to 65%, depending on the type of the RE configuration. Consequently, this increase in the LF lead to a reduction of the overall cost of the plant, as the increase on production yield overweight the investment and operational costs increase. The PV/Battery scenario was found to be the most optimal plant entailing 6 MW of PV coupled to 11 MWh of Battery capacity with a LCOA of 774 $/tNH3. Finally, a forecast estimate projected between 2021 and 2050, predicted a potential cost reduction down to 250 $/tNH3, and suggests that by the year 2030, green ammonia is expected to be economically competitive with the conventional fossil-fuel based processes.

Colonoscopy quality improvement after initial training: A cross-sectional study of intensive short-term training.

Background and study aims High-quality is crucial for the effectiveness of colonoscopy and can be achieved by high-quality training and verified with assessment of key performance indicators (KPIs) for colonoscopy such as cecum intubation rate (CIR), adenoma detection rate (ADR) and adequate polyp resection. Typically, trainees achieve adequate CIR after 275 procedures, but little is known about learning curves for KPIs after initial training. Methods This cross-sectional study includes work-up colonoscopies after a positive screening test with fecal occult blood testing (FIT) or sigmoidoscopy, performed by either trainees after 300 training colonoscopies or by consultants. Outcome measures were KPIs. We assessed inter-endoscopist variation in trainees and learning curves for trainees as a group. We also compared KPIs for trainees and consultants as a group. Results Data from 6,655 colonoscopies performed by 21 trainees and 921 colonoscopies performed by 17 consultants were included. Most trainees achieved target standards for main KPIs. With time, trainees shortened cecum intubation time and withdrawal time without decreasing their ADR, reduced the proportion of painful colonoscopies, and increased the adequate polyp resection rate (all P < 0.01). Compared to consultants, trainees had higher CIR (97.7 % vs. 96.3 %, P = 0.02), ADR after positive FIT (57.6 % vs. 50.3 %, P < 0.01), and proximal ADR after sigmoidoscopy screening (41.1 % vs. 29.8 %; P < 0.01), higher adequate polyp resection rate (94.9 % vs. 93.1 %, P = 0.01) and fewer serious adverse events (0.65 % vs. 1.41 %, P = 0.02). Conclusions Trainees performed high-quality colonoscopies and achieved international target standards. Several KPIs continuously improved after initial training. Trainees outperformed consultants on several KPIs.

Digital transformation projects for organisations: main stages and key performance indicators

This article describes the main goals and objectives of digital transformation, aimed at modernising and re-engineering business processes in companies in the current economic environment. The authors considered the stages of digital transformation and their impact on transformation in companies, it was revealed that digital transformation proceeds in several unique stages, reflecting the different stages of maturity and organisation of the business entity. In such a case, companies need to carefully assess the level of readiness of business processes for digital transformation, this is due to the fact that such processes are mostly highly innovative and involve high financial investments. The paper considers certain approaches to the economic justification of digital transformation projects in modern companies, namely feasibility study and financial and economic justification. The first approach is a set of research methods and making calculations to assess the economic, financial, technical, environmental and other conditions of investment. In turn, the second method is a description of the economic effect of its implementation, based on financial and/or statistical analysis of the current situation of the project, as well as related to the prediction of economic and other consequences of such projects. The authors have studied various recommendations for the application and analysis of KPIs; the recommendations of the Ministry of Digital Development of Russia, which include 20 KPIs, are widely used. In the concluding part of the article, the authors suggested that half of the indicators could be applied in the system of key performance indicators of the project, while some of them would be irrelevant.

Hydrogen-Fuel Cell Hybrid Powertrain: Conceptual Layouts and Current Applications

Transportation is one of the largest sources of CO2 emissions, accounting for more than 20% of worldwide emissions. However, it is one of the areas where decarbonization presents the greatest hurdles, owing to its capillarity and the benefits that are associated with the use of fossil fuels in terms of energy density, storage, and transportation. In order to accomplish comprehensive decarbonization in the transport sector, it will be required to encourage a genuine transition to low-carbon fuels and the widespread deployment of the necessary infrastructures to allow for a large-scale innovation. Renewable hydrogen shows potential for sustainable transportation applications, whether in fuel cell electric vehicles (FCEVs), such as automobiles, trucks, and trains, or as a raw material for ship and airplane synthetic fuels. The present paper aims to present how hydrogen-fuel cell hybrid powertrains for road vehicles work in terms of conceptual layouts and operating strategies. A comprehensive overview of real and current applications is presented, concerning existing prototypes and commercially available vehicles, with a focus on the main key performance indicators, such as efficiency, mileage, and energy consumption.

Health Information Systems’ Integration Project Using Rhapsody Engine

Background: This is an experiment report on Health Information Systems’ (HIS) integration project. It reports a successful implementation experience of HIS integration with the pharmaceutical Pyxis system that is used for medication dispensing. The Information Technology (IT) department in King Khalid Eye Specialist Hospital (KKESH) in the Kingdom of Saudi Arabia led this project to its final successful deployment. Rhapsody Engine successful adoption in the integration process helped KKESH achieve their productivity goals by improving hospital’s staff daily duties and clinical workflow. It also eliminated the system’s errors and alerting notifications. Objective: This experience paper discusses three main questions: What is the power of systems integration? What are the systems’ integration standards? What are the main key performance indicators for IT related projects? It also discusses the implemented solutions that solved many potential problems that could be encountered by similar big organizations such as the KKESH. Several drawbacks of having isolated internal systems will be discussed. Similarly, many difficulties encountered during the integration process were demonstrated in detail. Henceforth, this experiment report can be considered as a road map for medical organizations with similar size and limitations should they consider integrating their internal systems for better health care management and productivity

Analysis of Technologies for Carbon Dioxide Capture from the Air

The increase in CO2 concentration in the atmosphere has prompted the research community to find solutions for this environmental problem, which causes climate change and global warming. CO2 removal through the use of negative emissions technologies could lead to global emission levels becoming net negative towards the end of this century. Among these negative emissions technologies, direct air capture (DAC), in which CO2 is captured directly from the atmosphere, could play an important role. The captured CO2 can be removed in the long term and through its storage can be used for chemical processes, allowing closed carbon cycles in the short term. For DAC, different technologies have been suggested in the literature, and an overview of these is proposed in this work. Absorption and adsorption are the most studied and mature technologies, but others are also under investigation. An analysis of the main key performance indicators is also presented here and it is suggested that more efforts should be made to develop DAC at a large scale by reducing costs and improving efficiency. An additional discussion, addressing the social concern, is indicated as well.

Silicon solar cell production line and key performance indicators: A case of study at front size serigraphy stage

Photovoltaic industry has devices power improvement as a main target. This implies that technological advances are continuously implemented in production lines and their power improvements have to be monitored with the suitable key performance indicators. In this work, front size serigraphy design has been selected as process improvement and laminated unit power and cell to module ratio has been defined as the main key performance indicator. Real size silicon PV cells with three different front finger morphologies have been produced in industrial production lines by the use of two front size serigraphy designs. The modification of the finger dimensions (wide/height) from (183.0 μm/31.6 μm) to (184.0 μm/37.6 μm) and (140.0 μm/40.8 μm) leads to a redistribution of the majority produced cell power range from [4.10–4.15) W to [4.10–4.15) W and [4.20–4.25) W respectively. Concerning the cell production, it has successfully been monitored by the laminated unit power indicator along a month when shows an increment from 3.95 W to 4.20 W. Concerning module level, cell to module ratio per process cell range is selected as suitable indicator and monitoring during a year. In the specific case of [4.30–4.35) W cell range, cell to module ratio decrease from 7.7 % to 6.5 %

Array Gain Analyses of Single User MIMO in 5G NR PDSCH

In the last decade, Multiple-Input Multiple-Output (MIMO) algorithms have been developed for mobile communications networks to increase spectrum efficiency and reduce the transmitted power, which are also two of the main Key Performance Indicators of Fifth Generation New Radio (5G NR). Therefore, various MIMO algorithm are being researched for their suitability in 5G NR specifications. The objective of this study is to examine the performance gains achieved through the use of multiple transmit antennas and multiple receive antennas in 5G NR Physical Shared Downlink Channel (PDSCH). The study examines separately the array gains of Single-Input Multiple Output (SIMO) and Multiple-Input Single Output (MISO) then combines the transmitter and the receiver diversities in a MIMO system for 5G PDSCH. The array gains are achieved through precoding and combining vectors obtained by the use of Singular Value Decomposition (SVD). The results show that theoretical array gains are achieved in simulation environment.

Analysing GRI reports for the disclosure of SDG contribution in European car manufacturers

Social pressure has triggered companies to report their sustainable results. However, no clear methodology is available to easily report sustainable indicators directly related to the Sustainable Development Goals (SDGs).This paper aims at analysing sustainability reports from automotive companies with manufacturing facilities in Europe that issued sustainability disclosures in accordance with the Global Report Initiative framework.This paper maps a classification of the main sustainability metrics and their frequency amongst the leading car manufacturers reports. The main sustainable indicators usually disclosed are identified analysing the aspects covered. Consequently, the main Sustainability Drivers Key Performance Indicators (SDKPIs) for the car manufacturer sector are highlighted, including the relationship between them and the SDGs is shown. On the other hand, the main gaps in reporting these SDKPIs are indicated by considering the coverage of goals and targets, as well as the reporting frequency of current GRI metrics.This work is an early relevant work to provide a framework for analytics in the SDGs/GRI linkage and what flows from it. Moreover, it makes easier to compare the information disclosed. This development would help in harmonizing the language used and would also contribute to foster transparency, and hence to make sustainability efforts more visible.

ORGANIZATIONAL CULTURE TRANSFORMATION MODEL IN A MANUFACTURING PLANT: IMPACT ANALYSIS OF A FOUR YEAR JOURNEY TOWARDS HIGH PERFORMING ORGANIZATION

The current market environment, characterized by its volatility, uncertainty, complexity and ambiguity (VUCA), means a paradigm shift for any organization. Consequently being innovative via new perspectives such as the management of the Organizational Culture becomes a key factor as a potentially strong competitive advantage. In this framework, the main objective of this research is to analyse the impact of an Organizational Culture Transformation Model for an automotive sector’s manufacturing plant, with over 750 employees and 105,000 m2 in size, to facilitate its journey towards a High Performing Organization as way of survival. After four years of rigorous and systematic implementation of the model, the impact has been measured through the improvement obtained in the main business Key Performance Indicators and the Organizational Culture survey. In all of them, improvement exceeds expectations. Thus, Net Structural Cost Savings have been 1.4% versus 1% expected, Plant Overall Equipment Efficiency 69.8% vs 68%, for the Safety and Quality Index, Line Driven Management has been implemented as expected, and finally the Organizational Culture Survey (% of questions answered 4 or 5) has reached 58.1% vs 55%, demonstrating that the model positively impacts the organizational culture, enabling the viability of the plant. Keywords: Organizational Culture, High Performance Organization, Manufacturing Performance Improvement, Key Performance Indicators

Semi‐empirical development of a novel and versatile multiobjective optimization tool for co/trigeneration energy system design

This article describes a novel and implemented optimization routine for the analysis of combined cooling, heat, and power generation. The optimization model is briefly described both in terms of logic and operation and in terms of mathematical formalization. The model is able to optimize energy systems with different technologies used for the cogeneration unit, namely, SOFC and PEM fuel cells, internal combustion engines (ICEs), and micro-gas turbines (MGTs). The validation is performed both in terms of energy efficiency curves and in terms of unit costs, for all components within the energy system, including energy storage systems, heat pumps, and auxiliary boilers. The main key performance indicators included in the technical analysis of the CCHP units are the nominal electric and overall efficiency as a function of installed size and during a partial load operation, as well as the power-to-heat factor in dependence upon the nominal capacity/power. Two main storage technologies have been included, namely lithium-ion batteries (Li-ion BES) and lead-acid batteries (LA BES). The heat pump portfolio allows the choice between compression gas heat pumps (CGHPs) and absorption heat pumps (ABHPs), with their COP and EER described as a function of the external temperature and of a partial load operation. The validation of the modeled curves is performed in comparison with data of existing commercial units, presenting a correlation factor over 95% for most of the technologies. Highlights Presentation of a multiphysical design tool for CCHP systems; Economic, technical, and environmental analysis and optimization; PEMFC and SOFC are included in the tool technology portfolio; The model is based on validated and semi-empirical correlations; Evaluation of different energy storage systems and heat pump technologies. The present article describes the validation of an innovative multiobjective model aiming to optimize both the operating strategy of the plant and the sizing of the main equipment in a correlated way. The article focuses on the validation of the proposed model and, as a further novelty, includes a broad technological portfolio in terms of cogeneration units and describes the semi-empirical validation of the model by comparing the cost and performance curves modeled with existing market elements or literature data.