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A energia solar é considerada um dos recursos de energia renovável mais importantes para diminuir a dependência de combustíveis fósseis. Portanto, este estudo tem como objetivo realizar uma revisão sistemática da literatura sobre pesquisas de alto impacto sobre os impactos ambientais evitados com o uso da energia solar fotovoltaica no setor industrial para investigar tendências neste corpo da literatura. Para tanto, foram aplicados filtros ao portfólio bruto entre 209 artigos e selecionados 22 para identificar os estudos mais relevantes e de alto impacto na área. Os resultados mostram as principais tendências do referido corpo da literatura, as possíveis conexões da energia solar fotovoltaica e os impactos ambientais na indústria, bem como os principais investigadores que realizam os estudos. Este estudo fornece uma visão geral dos impactos evitados com o uso da energia solar fotovoltaica nas possibilidades de pesquisas futuras.

The demand for liquified natural gas grows as it is an energy resource that has more flexible means of transport than non-liquefied natural gas, and is more eco-friendly than other fossil fuels. This paper focused on comparing five different mixed-refrigerant liquefaction processes to determine which one is the most efficient in energetic and economic terms for four different natural gas processing scales (20,000 kg/h and 2, 4, and 8 MTPA of natural gas feed). Simpler processes with one or two refrigerant cycles (single mixed refrigerant, propane pre-cooled mixed refrigerant, and dual mixed refrigerant) and more complex processes with three cycles (mixed fluid cascade and AP-X) were analyzed. All processes were simulated in Aspen HYSYS, and the energy consumption was optimized by a communication between the simulator and an optimization algorithm based on Particle Swarm Optimization coded in MATLAB. The economic analysis was performed using the CAPCOST technique and the rule of six-tenths for upscaling. In the best-optimized scenario, the results show that the most complex process, AP-X, consumes the lowest amount of energy (0.2367 kWh/kg-LNG) while the simplest one, the single mixed refrigerant, consumes the highest value (0.2561 kWh/kg-LNG). In the economic analysis, the results indicate that energetically better processes may not provide a higher economic return even on large scales. The authors acknowledge the National Council for Scientific and Technological Development – CNPq (Brazil), processes 117948/2020-9, 307958/2021-3, 200305/2020-4, 148184/2019-7, 311807/2018-6, and 428650/2018-0, and Coordination for the Improvement of Higher Education Personnel – CAPES (Brazil) for the financial support. J.A. Caballero wants to acknowledge the financial support of the ‘Generalitat Valenciana’ under project PROMETEO 064/2020 and the Ministerio de Ciencia e Innovación, under project PID2021-124139NB-C21.

PurposeThe purpose of this paper is to present the application of Design of Experiments techniques for the analysis of operating parameters of an industrial oven for the heat treatment process of polyethylene terephthalate (PET) bottle filaments.Design/methodology/approachThe focus is on evaluating new ways of operating the transformation process. The main issue is to raise what are the variables interfering with the performance of the oven. The complete 2k factorial for three factors of control was used to analyze the behavior of these variables and their relationships in the specific response parameter for the process.FindingsThe results presented in this work allow the company to have greater knowledge about the operation of the equipment. The study showed possibilities of 14.8% energy reduction.Research limitations/implicationsThe heat treatment activity was characterized as a critical point in the production process, and techniques with empirical approaches, based on statistical techniques, was an opportunity that the company has to improve the execution of activities without major investments for the quality of the final product. The application of statistical quality techniques showed to be very promising.Originality/valueThe fact that the study was conducted using subjective quality performance makes this work different from others presented in the literature, showing the possibility to apply Design of Experiments using main control factors based on the opinion of experienced personnel involved in the process analyzed.

Energy integration among processes has been one of the most relevant approaches in the field of process synthesis, allowing for better energy use and leading to positive environmental and economic impacts in industrial terms. Since the Organic Rankine Cycle (ORC) is a power cycle indicated for using low quality heat as the heat source, this study aims at integrating an ORC with process streams to use the recovered waste heat as the heat source for the cycle. A thermoeconomic optimization in terms of net power produced was proposed and, by means of a case study, an improvement of 6% in specific net energy was achieved over the best value reported in the literature using n-hexane as the working fluid. For working fluids pentane and benzene, improvements of 4.7% and 8.5% were obtained relative to the best specific net energy value presented so far.

Heat exchanger network (HEN) control is considered to be a difficult task due to its nonlinear behavior, complexity, presence of disturbances and noise. The optimal operation of HEN implies the implementation of a robust control system able to overcome all these issues. This work presents an advanced technique based on an artificial neural network model predictive control (NNMPC) and compares the controlling performance to three widely used controllers: two PID and a linear MPC. The goal is to control an outlet oil stream temperature of a HEN containing four heat exchangers in counter-current. A lumped parameter model was used to describe the HEN system. The data generated were used to train a neural network in the Matlab environment using the Levenberg-Marquardt algorithm. Closed-loop negative and positive step responses were simulated to investigate set point tracking and disturbance rejection. The ability to handle constraints was evidenced in MPC, as well as faster settling time and minimum overshoot.

A surrogate-based multi-objective optimization framework is employed in the design of natural gas liquefaction processes using reliable, black-box process simulation. The conflicting objectives are minimizing both power consumption and heat exchanger area utilization. The Pareto solutions of the single-mixed refrigerant (SMR) and propane-precooled mixed refrigerant (C3MR) processes are compared to determine the suitability of each process in terms of energy consumption and heat exchanger area. Kriging models and the ɛ-constraint methodology are used to sequentially provide simple surrogate optimization subproblems, whose minimizers are promising feasible and non-dominated solutions to the original black-box problem. The surrogate-based ɛ-constrained optimization subproblems are solved in GAMS using CONOPT. The Pareto Fronts achieved with the surrogate-based framework dominate the results from the NSGA-II, a well-established meta-heuristics of multi-objective optimization. The objective functions of non-dominated solutions go as low as 1045 and 980.3 kJ/kg-LNG and specific UA values of 212.2 and 266.9 kJ/(°C kg-LNG) for SMR and C3MR, respectively. The trade-off solutions that present the minimum sum of relative objectives are analyzed as well as the dominance of C3MR over SMR at low power consumption values and conversely at low heat exchanger area utilization. The authors LFS, CBBC, and MASSR acknowledge the National Council for Scientific and Technological Development–CNPq (Brazil), processes 200305/2020-4, 148184/2019-7, 440047/2019-6, 311807/2018-6, 428650/2018-0, 307958/2021-3 and Coordination for the Improvement of Higher Education Personnel–CAPES (Brazil) for the financial support. The author JAC acknowledges financial support from the “Generalitat Valenciana” under project PROMETEO 2020/064 and the Ministerio de Ciencia e Innovación , under project PID2021-124139NB-C21.

Sugarcane biofuel processing plays an important role in the Brazilian economy, being Brazil the world’s largest sugarcane ethanol producer. Nevertheless, the success of its distilleries depends on how they overcome the new scientific challenges faced, mainly regarding the disposal or reuse of sugarcane vinasse. In this sense, the present work uses Aspen Hysys® to simulate an innovative synthesis gas production process integrated into an ethanol distillery. Initially, the distillery is simulated and validated using industrial real data from a plant located in Brazil in order to analyze the potential of sugarcane vinasse generation. Thereafter, conventional (from natural gas) and renewable (from vinasse-based biomass) routes for syngas production through steam methane reforming are compared considering both, technical feasibility and economic investment. Such comparison presents a more rigorous simulation of the vinasse-based plant and shows that the proposed scenario might be 27.87% less expensive than the plant from natural gas. The production plant of syngas from vinasse-based biogas is evaluated to further improve the thermal efficiency and financial performance of the process designed. The results demonstrate that syngas production coupled to ethanol plants is a promising market to countries ascending in technology development for biomass utilization, such as Brazil.

A secondary circular settling tank (SCST) with low hydraulic load was numerically analyzed. A transient, three-dimensional model was employed to evaluate flow in a pilot plant. Three phases were considered: clean water was a continuous phase; sludge was a dispersed phase and an air layer under atmospheric conditions was considered above the surface of the water at the top of the tank. Height and diameter of the baffle located in the tank inlet was studied in order to reduce the resuspension of sludge particles. High velocity magnitude and turbulent kinetic energy cause resuspension of solids in the center of the bottom of the tank. With the sludge outlet closed, after 30 min of simulation there was an increase of approximately 1.0% in sludge concentration. The results provided detailed insight into the hydrodynamic flow within the SCST.

In the present work, a bi-objective optimization model is proposed for the green management of the supply chain of fresh fruit considering transportation costs, and environmental impact categories given by the ReCiPe methodology. The e-constraint method is used to convert the bi-objective function into a single-objective optimization problem and it is applied to two case studies to test the model in a tomato supply chain, providing a set of Pareto solutions. Results showed that the most affected environmental impact category is “climate change” from the emission of greenhouse gases and that there are greater CO2 emissions at the stage of transportation from producers to warehouses. Solutions obtained by the proposed approach provided useful information such as the best operating points for the green management of the supply chain. Moreover, the model can be used in similar situations for regional development.

Water distribution networks (WDNs) are the main component of industrial and urban water distribution systems and are currently formed by pipes, nodes and loops. In this article, a deterministic mathematical programming approach is proposed, aiming to minimize the cost of looped WDNs, considering known pipe lengths and a discrete set of commercially available diameters. The optimization model constraints are mass balances in nodes, energy balances in loops and hydraulic equations, in such a way that no additional software is needed to find the appropriate pressure drops and water velocities. Generalized disjunctive programming is used to reformulate the discrete optimization problem to a mixed-integer nonlinear programming (MINLP) problem. The GAMS (General Algebraic Modeling System) environment is used to solve the problem. Four cases are studied to test the applicability of the model and the results show compatibility with the literature. This study was financially supported by the Coordination for the Improvement of Higher Education Personnel (CAPES) (Brazil) [processes 88887.217374/2018-00 and 88881.171419/2018-01] and the National Council for Scientific and Technological Development (CNPq) (Brazil) [processes 428650/2018-0 and 440047/2019-6]; and by the Conselho Nacional de Desenvolvimento Científico e Tecnológico and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

Sugarcane can play an important role in helping to solve many of the economic and environmental challenges as it has the potential of creating jobs, lowering carbon dioxide emissions, enhancing energy security, and reducing global dependence on fossil fuels. This paper presents a mathematical approach to optimize the Brazilian supply chain (SC) of sugar and bioethanol, using sugarcane as raw material. SC management of sugarcane-based products can be treated as a bi-objective optimization problem with a MILP formulation, in order to find solutions to balance economic profits and environmental damage. A mathematical model is developed and implemented in GAMS, comparing seven different production technologies. The results indicate a set of optimal SC possibilities for the Brazilian scenario and that both quantities and selection of technologies indicated by Pareto optimal solutions are distinguished, outlining three different configurations: environmental, economic, and a balanced scenario. The distribution of technologies in each of the Brazilian regions is presented in these three distinct scenarios indicating that, to approximate the current chain to a balanced solution, it is necessary to migrate technological plants of only sugar products to mixed products technologies (i.e., sugar and bioethanol) and to complement bioethanol production with autonomous distilleries, especially of anhydrous bioethanol production. In this scenario, governmental authorities, sector investors, and decision-makers can plan the implementation of new plants based on the recommendations of this study.

Brazil is the largest producer of sugarcane ethanol in the world. Among the technologies that aim to improve the process, second generation ethanol is one of the most promising. Although still in development, it is a consensus that this process must be integrated into the production of first generation ethanol. Most of the work on this subject is involved in evaluating the steady-state process. However, the knowledge of the process dynamics plays an important role on the operational efficiency and control. In this work multiple effect evaporation is addressed. A dynamic phenomenological model of this step was developed in EMSO process simulator. A stream of glucose syrup is mixed with sugarcane juice stream at the entrance of the system and simulations were performed to analyze the integrated system dynamics. Step disturbances were applied in the sugar concentration, volumetric flow rate and temperature of the juice individually and simultaneously. Glucose syrup concentration and flow rate was also disturbed. PI/PID controllers were tested in order to control the system and the model consistency was verified in Aspen Dynamics. The analysis suggests that an adequate control system at the liquid and vapor output of each vessel cannot be achieved using only classical controllers.

Optimal operation of integrated heat exchangers is a challenging task in the field of process control due to system nonlinearities, disturbances and adequate model identification. This paper describes the design of an advanced neural network predictive control (NNPC) applied to a heat exchanger network. A case study with two hot and one cold streams, through three counter-current heat exchangers is used to test the proposed strategy. A lumped dynamic model is built based on the concept of multi-cells topology (mixed tanks), where the hot and cold cells are connected by a wall element throughout the heat exchanger length. Each cell is assumed perfectly mixed and all physical properties are constant. A distributed behavior is achieved by increasing the number of cells. The main assumptions of the lumped model are constant temperature in each cell, heat exchanger volume and area equally distributed between cells and negligible heat loss to the environment. The predictive controller relies on a neural-based model of the plant that is used to identify the system and to predict future performance over a predefined horizon. Results were compared to a traditional controller, and the control performance was improved when compared to the Ziegler-Nichols tuning method.

Work and heat integration has emerged in the last decade as a promising area with a wide scope for research. Under that relatively new field, the synthesis of work and heat exchange networks (WHEN) is certainly an encouraging subject. This work presents a method for WHEN synthesis considering some aspects that were not approached in previous literature. Multiple electricity cost scenarios are considered and analyzed in the synthesis task. These are proposed according to different electricity production regimes, which are used by suppliers due to eventual changes in electricity production conditions (e.g., low level in hydroelectric power plants reservoirs). Evidently, monthly electricity prices are affected under such regimes. In the model, multiple utilities use is possible. Utilities can be modelled as regular process streams (instead of the typical approach of fixed inlet/outlet temperatures in heaters/coolers). The model also comprises the use of piecewise linear approximations for enthalpy/temperature calculations. The solution approach is an enhanced meta-heuristic method. It could be observed that considering an intermediate electricity cost scenario (between optimistic and pessimistic predictions) for the WHEN synthesis stage may lead to smaller monetary losses in case of occurrence of other scenarios.

Retrofit of heat exchanger networks (HEN) is an important subject in the heat integration field. Several studies on the matter use Pinch-based techniques and mathematical models solved with deterministic methods. This work aims at providing an alternative method based on a model derived from a broad superstructure solved with a meta-heuristic approach. The referred superstructure comprises features not present in most HEN studies based on mathematical programming. Structural options such as the possibility of streams sub-splitting, partial mixing, serial units at a single stream branch and the allocation of heaters/coolers at intermediate positions are possible. The methodology was applied to three large-scale industrial case studies from the literature, based on oil refineries. Two of them contained streams with variable heat capacity. In all examples, the present methodology was able to outperform previous studies regarding total annual costs, proving an interesting alternative to methods that are currently applied in the literature.

This paper presents a new algorithm for the design of heat exchanger networks (HEN) that tries to take advantage of the strengths of the sequential and simultaneous approaches. It is divided into two sequential parts. The first one is an adaptation of the transportation model (TransHEN). It maintains the concept of temperature intervals and considers the possibility of heat transfer between all the hot and cold streams inside those intervals, and at the same time it allows the a priori calculation of the logarithmic mean temperature difference between all possible heat exchanges, and therefore it maintains the area estimation linear in the model. The second step (HENDesign model), uses a superstructure that contains all the possible alternatives in which the matches predicted by the first stage model can exchange heat to design the final heat exchanger network. Unlike the sequential approach, in this model, all heat flows, temperatures, areas, etc. are reoptimized maintaining the set of matches predicted in the first stage. The model is highly nonlinear and nonconvex, however, it is relatively easy to get good results, because the model starts with the values predicted by the TransHEN model. The algorithm has been tested using fifteen benchmark problems commonly used in literature to compare the performance of heat exchanger network algorithms. In eleven out of the fifteen cases present better or equal results than the best ones reported in the open literature. In three the results presented only marginal differences in total annualized cost (lower than 0.5%) and only a difference of 2.4% in the largest one. The authors gratefully acknowledge financial support from the «Generalitat Valenciana: Conselleria de Educación, Investigación, Cultura y Deporte-Spain» under project PROMETEO 2020/064 and from the “Conselho Nacional de Desenvovimento Científico e Tecnológico-CNPq–Brazil, under projects 428650/2018-0 and 311807/2018-6.”

The silvipastoral systems are characterized by the association between tree crops, pastures and animals and can also constitute an efficient and sustainable means of supplying forest biomass for energy purposes such as electric, mechanical and thermal energy generation. It is an unconventional energy alternative and the evaluation of the energy potential offered by this productive system depends on several factors, such as management techniques, forest species, silvipastoral system characteristics and the design of the conversion and energy utilization process. In this context, it was developed a mathematical model to determine the energy efficiency of silvipastoril production system integrated with a cogeneration system for the production of thermal, mechanical and electrical energy. It can be concluded that these results are advantageous in relation to the conventional modalities of energy generation, taking into account the prices of electricity practiced in the market.

This paper presents a surrogate-assisted framework to solve constrained black-box optimization problems, which is applied to the optimal single-mixed refrigerant natural gas liquefaction processes design. This approach uses kriging models as cheap-to-evaluate, reliable surrogates of the process-simulator-dependent black-box functions of the optimization problem. From kriging prediction and estimated error, the probability of feasible improvement acquisition function is derived and optimized to search forfeasible and improving candidates to perform local search in the penalized simulation. The kriging-assisted global search scheme is efficient to find promising candidates for local refinement, and it improves previous results that used only local optimization by 3.36 and 5.32 %. The surrogate model reduces the time to evaluate the functions from 0.9933 ± 0.0815 to 3.515e-4 ± 8.353e-5 seconds. The kriging approximation mean squared errors assessed in a k-fold cross-validation is 7.271e-7 ± 1.297e-7 for the objective function and 0.3099 ± 0.0856 for the constraint. The authors acknowledge the National Council for Scientific and Technological Development–CNPq (Brazil), processes 148184/2019-7, 440047/2019-6, 311807/2018-6, 428650/2018-0, 305055/2017-8, and Coordination for the Improvement of Higher Education Personnel–CAPES (Brazil) for the financial support.

The optimal design of single mixed refrigerant (SMR) natural gas liquefaction process is decisive for upgrading the liquefied natural gas (LNG) value chain and can be achieved using process simulators and derivative-free optimization techniques in simulation–optimization frameworks. The process work consumption can be reduced by modifying process flowsheet and manipulating refrigeration cycle thermodynamic conditions and refrigerant composition. The goal of this paper is the design of energy-efficient SMR liquefaction process for LNG production using simulation–optimization approach, in Aspen HYSYS® and MATLAB®, that considers more decision variables to be optimized with a Nelder–Mead simplex algorithm and small flowsheet modifications, such as hydraulic turbines and flash separators in between compression stages. The present approach resulted in liquefaction process with network duty of 750.2 kJ per kg of natural gas, which is considerably smaller than those reported in the literature. The authors acknowledge the National Council for Scientific and Technological Development-CNPq (Brazil), processes 148184/2019-7, 440047/2019-6, 311807/2018-6, 428650/2018-0, 305055/2017-8, and Coordination for the Improvement of Higher Education Personnel-CAPES (Brazil) for the financial support.

Energy integration is a matter of substantial importance in process engineering. Much attention was given in the last decades, in special, to heat recovery. However, recently, simultaneous work and heat recovery has arisen as an important area of study in process engineering. It has been demonstrated that work and heat exchange networks (WHEN) may improve the energetic efficiency of processes such as liquefied natural gas (LNG) production. Optimization-based frameworks for WHEN synthesis developed so far are based on deterministic approaches and commercial solver implementations. This work aimed to present a novel alternative framework based on a matrix-based implementation fit for the use of a meta-heuristic solution approach. The superstructure employed is inspired on previous literature and extended for a broader analysis. The method was applied to three examples, being two based on real processes. In benchmark tests, the approach was able to outperform all other solutions reported with respect to total annual cost, the objective function adopted in these studies.

In the present paper we propose an optimization model based on fuzzy-Analytic Hierarchy Process (AHP) programming in which the external costs associated with the environmental impacts caused by energy generation and equipment construction were explicitly incorporated in the synthesis of Heat Exchanger Networks (HEN) using the eco-costs methodology. The damage categories that compose the environmental and economic objective functions were human health (HH), ecosystem quality (EQ), resources depletion (RD), greenhouse gases (GW) impacts and HEN cost. The inclusion of the impact in the optimal synthesis of HEN was performed considering the most used fuels in industries: hard coal, heavy and light fuel oil and natural gas. Boilers operating with each one of the cited fuels were considered, and three different technologies for boiler operating with natural gas were included (low NOx natural gas, modulating and non-modulating natural gas boilers). The weights were obtained through a questionnaire directed to a multidisciplinary group of specialists. A case study with two hot and one cold streams was used to test the efficiency of the model and results were compared to a single-objective model and to a model not considering impacts in the objective function. Results showed that the fuzzy-AHP programming model was not able to find HEN costs lower than those obtained by the single-objective model for HENs operating with heavy and light fuel oil. For the HENs operating with hard coal and natural gas, HEN costs were lower than those obtained by the single objective model. The same HEN configuration was obtained for all fuels considered.

Several optimization models for process synthesis and optimization are derived from superstructures. Those used for Heat Exchanger Networks (HEN) must comprise consistent design options so that efficient structures may be found. The complexity arisen from nonlinearities and non-convexities in the mathematical models for HEN synthesis, though, hampers the use of superstructures with elaborated streams splits and mixing alternatives as well as multiple options for devices allocation. In this work, a new superstructure based on a previous stage-wise one is presented encompassing, for each process stream, the use of splits, sub-splits, cross flows (i.e., partial stream mixing), serial heat exchanger allocation in single stream split branches and heaters/coolers allocation in intermediate stages of the structure (i.e., before process streams heat exchangers). The meta-heuristic approach used to solve the optimization model was able to achieve solutions better than those previously reported in the literature for five case studies, demonstrating efficiency in solving such complex model.

Heat Exchanger Networks (HEN) synthesis is a process engineering problem that can be mathematically characterized as highly combinatory, non-linear and non-convex. All these aspects bottleneck the identification of locally optimal solutions at acceptable computational time. This work proposes an optimization algorithm based on a superstructure considering non-isothermal mixing and stream splitting. HENs are optimized through the application of a bi-level new hybrid method that works at an upper level with Genetic Algorithm (GA) to optimize discrete variables and at a lower level with Differential Evolution (DE) for optimizing heat loads and stream split fractions in order to find solutions with low total annual costs (TAC). The proposed method was applied to six literature case studies and was efficient in obtaining solutions with TAC comparable or lower than those previously reported.

The objective of this study was to evaluate the biosorption dynamics of heavy metals by the dead macrophyte Egeria densa in fixed bed column. Experiments were conducted with mono- and bi-component solutions of Cd(II) and Zn(II) at pH 4.0, temperature of 30 °C and volumetric flow rate range between 2 and 6 cm3 min−1. A mass transfer mathematical model was applied for describing the mono-component biosorption dynamics. In this model, the equilibrium was represented by the batch Langmuir isotherm, and intraparticle diffusion was considered as the rate-limiting step for the mass transfer. The mass transfer coefficient (KS) was estimated by the downhill simplex optimization method for each volumetric flow tested, while the axial dispersion coefficient was calculated by empirical correlations. The KS coefficients and the equilibrium parameters obtained in a mono-component batch system were validated using data from the breakthrough curves of the Cd(II) and Zn(II) binary mixture obtained for different volumetric flows and initial concentrations of each metal ion.

Fouling is one of the main causes of industrial problems regarding operation and control of shell and tube heat exchangers. Although fouling is a time dependent phenomenon, most papers in the literature focus on fouling mitigation in steady-state heat recovery. In this work, a lumped parameter model to describe time-varying conditions and their influence in controller tuning is presented. For each modeling cell, there are four input variables (inlet temperatures and flow rates for shell and tube sides), which generate two output signals (outlet temperatures for shell and tubes). The influence of fouling in process control is evaluated by considering intermediate values of thermal resistance of fouling (Rf), simulating its variation with time. The model was implemented in MATLAB/Simulink, and simulations have been carried out for different periods of operation. A step change was applied in the shell flow rate to evaluate the response in the tube outlet temperature. Results show that periodic fitting in pro...

In this work, a mathematical approach for optimizing and planning the Brazilian bioethanol supply chains (SC) is presented. The optimization problem has an MILP formulation, aiming to maximize the net present value (NPV) of the entire SC of the sugar and bioethanol sector in Brazil. The model takes into account seven different production technologies, two types of warehouses, three types of transportation modes and seven exportation options, whose data were obtained from Brazilian industrial practices. The model aims to propose the optimal configuration of a bioethanol network, that is, the locations of the production and storage facilities, their capacity of expansion policy, the technology selected for manufacturing and materials storage and the flows of all feedstock and final products involved in the bioethanol SC in Brazil. A comparison between the current situation of Brazilian bioethanol SC and the optimal configuration achieved by the proposed model is also included.

In a plant, different utilities may be produced from different sources whose costs vary singularly. Therefore, evaluating the financial risk associated with heat exchanger networks (HEN) with multiple utilities is essential. This work aims to address the HEN synthesis under uncertainty by presenting several optimization models for managing financial risk. To this end, three different risk metrics are employed: variability index, downside risk, and risk area ratio. The resulting models are based on an enhanced version of the stage-wise superstructure, which incorporates the potential to place utilities at all HEN stages. The study considers fixed utility sources as well as the possibility to vary those yearly. Furthermore, we propose a novel “pseudointersection” calculation scheme for the risk area ratio metric, which proves efficient in assessing how risks can be reduced at minimal opportunity loss. The proposed models are able to efficiently identify solutions appealing to different investor profiles.

This paper presents a framework to solve the constrained black-box simulation optimization problem that arises from the optimal energy-efficient design of single-mixed refrigerant natural gas liquefaction process using reliable process simulator. Kriging surrogate model is used to introduce simple, computationally inexpensive, and effective algebraic formulations with reliable derivatives to the black-box objective and constraints functions. The algebraic surrogate optimization problem is embedded into a nonlinear programming (NLP) model in General Algebraic Modeling System (GAMS). The NLP problem is solved using efficient multi-start gradient-based optimization with CONOPT local solver to determine a candidate of decision variables for which the true functions are calculated in the rigorous simulation. The single-mixed refrigerant process is analyzed considering one-to-three-stage expansion and phase separation to assess potential energy savings. The present approach results show that more expansion stages can provide energy savings from 12.02 to 14.70 % comparing two-stage and three-stage expansion system with single-stage. This optimization framework is more effective and consistent than Particle Swarm Optimization and Genetic Algorithm given the same budget of simulation evaluations for the considered simulation optimization problems, resulting in 13.57 to 53.26 % of energy savings. The authors acknowledge the National Council for Scientific and Technological Development—CNPq (Brazil), processes 148184/2019-7, 440047/2019-6, 311807/2018-6, 428650/2018-0, and Coordination for the Improvement of Higher Education Personnel—CAPES (Brazil) for the financial support.

The prediction of important control properties is a challenging task for shell and tube heat exchangers design. Dynamic models, which include fouling effects, are still poorly investigated in the recent literature. To study the behavior of variable conditions over time and its influence on the dynamic aspects of the system, a design approach based on TEMA standards has been proposed, aiming to analyze the process in a more realistic equipment. To study the behavior of time-varying conditions and its influence in the system dynamics, the model considered in this work is based on the idea of heat exchanger cells as the basic modeling element. This kind of approach has some advantages over the distributed model, such as continuous variables in time and discrete in space, leading to ordinary differential equations (ODE) and also providing the possibility of controlling the model complexity by adjusting the number of modeling cells. The ratio between output and input signals in each cell generates eight operat...

Several methods for heat exchanger networks (HEN) design are based on the use of superstructures. The models they give rise to can lead to different design options to be explored in HEN synthesis. In this work, a stage-wise superstructure with new features for the optimal placement of heaters and coolers, including the possibility of the use of multiple utilities, is presented. In the model, those units can be placed in different stream split branches in all stages, differing from the usual allocation at stream ends. Such possibilities yield a more complex mathematical model. To solve it, an improved version of a previously presented hybrid meta-heuristic method was used. Three examples from the literature were studied. The use of the superstructure with the additional utility-related options, as well as the enhanced meta-heuristic solution method, led to configurations with lower associated total annual costs than those reported in previous works.

Multiperiod heat exchanger networks (HEN) are required in plants with seasonal alterations to operating conditions. Like for single-period HEN, the synthesis of multiperiod HEN can be formulated as a mathematical programming optimization problem. However, since the network needs to feasibly perform heat integration under different process conditions, additional constraints are required and problem complexity is increased. Studies on the subject based on mathematical programming often use deterministic approaches and rely on commercial solvers. In this work, a meta-heuristic two-level method based on Simulated Annealing and Rocket Fireworks Optimization (SA-RFO), originally developed for single-period HEN synthesis, is adapted to handle multiperiod HEN optimization. A new post-optimization (PO) strategy is coupled with the main method in order to improve the results. Four case studies are investigated and results are compared to the literature. The solutions achieved presented lower total annual costs (TAC) than those obtained by other methods and the new PO scheme was able to significantly improve the results.

Plants with cyclical variations in operating conditions require multiperiod heat exchanger networks (HEN) able to perform heat integration efficiently in all those periods. In such cases, however, overdesign issues arise, i.e., a heat exchanger might be too large for the service it must perform in a given period, requiring fractions of the process streams to by-pass such units. In this work, timesharing mechanisms (TSM) are considered for circumventing such issues, which means heat exchanger services are not fixed throughout the different periods of operation and a single piece of equipment can perform heat integration between different pairs of streams in each cycle. Two schemes are considered with the present meta-heuristic approach to obtain a preliminary solution before applying the service switching concepts. Then, systematic service rearrangement and equipment re-sizing in a new optimization stage are performed in order to find areas more suitable for the different timesharing arrangements. The latter is a new proposed model, not present in previous works that consider timesharing schemes. Four cases from the literature were investigated and results with lower total annual costs (TAC) than those reported previously by other works were achieved.

Although various Heat Exchanger Network (HEN) synthesis methods have been proposed in the literature, fundamental study for addressing uncertainties arisen from market fluctuations using stochastic variables and parameters is scarce. Such feature certainly adds difficulties to a problem already not straightforward to solve. In that manner, this work adapts a meta-heuristic approach to be able to efficiently perform such task. Uncertainties are assumed from variations in costs of commodities related to production of utilities. Several forecast scenarios are generated via Monte Carlo Simulation in order to obtain discretized distributions for the uncertain variables. Five financial risk metrics are applied for risks management. Each metric is formulated as secondary function to expected total annual costs (ETAC) in a multi-objective optimization (MOO) model with two objective functions. A benchmark case study is adapted in order to demonstrate the method reliability. The approach is able to achieve results that fit for different types of investors ( e.g. , risk-averse, risk-taker), handling uncertainty by efficiently performing trade-offs in heat exchange areas and utilities requirement.

The Model Based Predictive Control (MPC) is an advanced control technique that can deal with of multivariable systems with interactions and considerable dead times, nonlinearities and restrictions on the variables. Different types of objective functions can be used in the MPC control algorithm with specific parameter settings for each type of objective function. In this work a MPC control strategy for distillation column using an objective function with economic cost was implemented. The MPC controller with the objective function proposed worked properly, stabilizing the distillation column when it has undergone a change in the setpoint and also a series of disturbances in the feed. Using energy point of view, the implemented controller is more efficient than the standard MPC controller.

Heat Exchanger Network (HEN) synthesis approaches based on total annual costs (TAC) optimization can reduce CO2 emissions and electricity used in plants, since the use of utilities is reduced with a cost-optimal design. However, environmental impacts (EI) are not explicitly addressed in such methods. Life cycle assessment (LCA) metrics can quantify EI and can be used as objective function. This work proposes a meta-heuristic approach to perform a multi-objective optimization (MOO) of TAC and EI in medium and large-scale HEN. The method developed efficiently achieves near-Pareto fronts for four industrial-size case studies. Optimal-TAC and optimal-EI solutions, as well as configurations with low EI but still competitive TAC are presented. Cost-optimal solutions reported in the literature are compared with non-dominated solutions obtained in this work with similar TAC. In all cases, the approach developed is able to achieve solutions with remarkably lower EI, demonstrating the importance of multi-criteria optimization in HEN synthesis.

A substantial amount of research on heat exchanger networks (HEN) synthesis has concentrated on minimizing the total cost as unique criterion, thereby disregarding their negative environmental effects. In the present paper the external costs associated with the environmental impacts caused by energy generation and equipment construction were incorporated explicitly in the HEN synthesis task using the eco-costs methodology. The inclusion of the impact costs in the optimal synthesis of HEN was performed considering the most used fuels in chemical and petrochemical industries: hard coal, heavy and light fuel oil and natural gas. Boilers operating with each one of these fuels were considered, and three different technologies for boiler operating with natural gas were included (low NO x natural gas, modulating and non-modulating natural gas boilers). The influence of the impact assessment on total area, area cost and HEN cost were observed. Results show that the inclusion of indirect costs (externalities) in the optimization procedure can affect significantly the characteristics of the minimum cost design, the operational conditions and HEN topology. HEN operating with hard coal has the second lowest HEN cost, but the higher impact cost. Greenhouse gases and ecosystems impacts had the largest costs for all the cases considered. The HEN operating with heavy and light fuel oil have the highest HEN costs and the second and third total impact costs and this HEN may become less competitive when compared with cheaper fuels, due to its relatively high cost. HEN operating with natural gas is the most competitive alternative, since it has the lowest total impact cost and HEN area cost. For these fuels, results demonstrate that the reduction of total impact is affected more by the increase of the total area than by the utilities consumption.