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• A mathematical model is proposed for tissue paper drying process. • The model realizes the accurate simulation of key parameters in the drying process. • The model provides global optimization scheme for key operating parameters. • The model improves the competitiveness by reducing 8.72% of the drying cost. The drying process accounts for the largest proportion of energy consumption in paper mills. Energy system optimization has a great significance for reducing the energy consumption of the paper drying process. The drying process is a complex system that consists of several subsystems, such as cylinder and air hood systems. Previous optimization models for energy systems usually focused on these subsystems. A global optimization methodology for the entire drying process is lacking, and no existing models can be applied in practice. In this work, an energy system optimization model for the tissue paper drying process is proposed based on a process simulation model. The modeling process integrates the various subsystems and fully considers the coupling of the paper drying process, which greatly enhances the industrial application value of the model. Industrial operating data are used to test the simulation model, and the results show that the simulation error of each key variable is within 5%, which meets the real-world production requirements and lays the foundation for an energy efficiency analysis of each subsystem. Applying the optimization model to a tissue paper mill, the results show that it can reduce drying costs by 8.71%. [ABSTRACT FROM AUTHOR]

• Introduction of a control strategy for batch tablet film-coating processes. • Effective use of available process data for online control of critical variables. • Use of a dynamic process simulator to validate a decentralized PI controller. • Potential reduction in total batch processing time and energy consumption. This paper proposes a control strategy for batch tablet film-coating processes using readily available process instrumentation. Outlet air conditions (temperature and relative humidity), which impact coating quality, are controlled by the manipulation of inlet air temperature and coating spray rate. Controller design, tuning and validation rely on the use of a dynamic process simulator, calibrated using data collected from a pilot plant coating unit. The final 2 × 2 decentralized PI controller demonstrated good performance for both setpoint tracking and disturbance rejection in simulation. Additionally, controlled variable setpoints were determined to maximize plant profits considering batch duration and energy consumption as optimization criteria. For the case study considered, the total batch processing time can potentially be reduced by 40 % while simultaneously reducing energy usage by 30 %, in comparison with the more conservative fixed recipe approach typically adopted in production. [ABSTRACT FROM AUTHOR]

Given the significant energy consumption and carbon emissions associated with buildings, there is increasing interest in improving sustainability of building operations to reduce impact on climate change. A common goal is to operate buildings as "net-zero" energy users in which all energy consumed by the building is balanced against renewable energy purchased from the grid or produced on site. To achieve net-zero status, many buildings will require significant retrofit so as to both reduce energy consumption in the absolute sense and provide the remainder without consuming fossil fuels. Thus, multi-year planning is required to ensure that goals can be met on time. In addition, due to the inherent uncertainty associated with energy consumption and generation, actually achieving net-zero energy use may require discretionary curtailment actions to be taken, and deciding whether such actions are necessary can be challenging. To address these challenges, we propose in this paper a design optimization and operational planning strategy to make the decisions needed to achieve sustainability goals in buildings. The strategy can be applied to schedule design changes over a long horizon to meet annual targets, and it can also be applied in closed loop on a shorter horizon to determine whether curtailment is needed to stay on track. We discuss the formulation of the optimization problem, solution methods, and modeling approaches for key parameters. Application of the strategy is illustrated via examples. Overall, this approach will help automate planning that is often done manually, allowing buildings to take a significant leap forward toward achieving their sustainability goals. • Achieving sustainability goals in buildings requires multi-year planning. • The required design and operational decisions can be made via optimization. • Key problem parameters can be obtained from data-driven modeling. • The strategy allows sustainability goals to be met at minimum cost. [ABSTRACT FROM AUTHOR]

In this paper, authors propose a contribution for improving the open innovation processes. It shows the necessity to get an efficient methodology for open innovation in order to build a computer aided tool for inventive design in Process Systems Engineering (PSE). The proposed methodology will be evocated to be fully used in the context of the “revolutionary” concepts around the so-called factory for the future, also called integrated digital factory, innovative factory… As a result the main contribution of this paper is to propose a software prototype for an Open Computer Aided Innovation 2.0. By definition this open innovation relies on collaboration. This collaboration should enable a community, with a very broad spectrum of skills, to share data, information, knowledge and ideas. As a consequence, a first sub objective is to create a methodological framework that takes advantages of collaboration and collective intelligence (with its capacity to join intelligence and knowledge). Furthermore, the raise of the digital company and more particularly the breakthroughs in information technologies is a powerful enabler to extend and improve the potential of collective intelligence. The second sub objective is to propose a problem resolution process to impel creativity of expert but also to develop, validate and select innovative solutions. After dealing with the importance of Process Innovation and Problem solving investigation in PSE, the proposed approach originally based on an extension of the TRIZ theory (Russian acronym for Theory of Inventive Problem Solving), has been improved by using approach such as case-based reasoning, in order to tackle and revisit problems encountered in the PSE. A case study on biomass is used to illustrate the capabilities of the methodology and the tool. [ABSTRACT FROM AUTHOR]

• determine the optimal capacity, type, number and location of renewable energy resource • Optimal location of the switch for clustering the traditional DN into a set of interconnected MGs • Considering multiple function contains; operation cost, reliability, network structure and environmental factors together • Considering uncertainty to generate and reduce scenario-based modelling. In this paper, a scenario-based strategy for optimizing multiple microgrids has been proposed by considering the demand and renewable energy sources (RES) unit unpredictability. The approach seeks to determine the appropriate capability, category, number, and position of renewable and controllable distributed RES, and the ideal switch site for clustering the traditional distributed network (DN) into a collection of interconnected micro-grids (MGs) with an economical and trustworthy structure. The suggested solution attempts to minimize all plans, counting asset and process expenses, system loss, air pollution, and the cost of microgrid s' unsupplied energy. The Monte Carlo method was adopted to generate and reduce the scenarios, and the Newton-Raphson method was used for power flow. To optimize the system, we used the improved shark smell optimization algorithm in the simulation. Obtained results of economic and reliability assessment demonstrate that by dividing the traditional DNs into a set of multiple MGs, the reductions in reliability costs and technical costs are provided due to the reduction in losses and the improvement of the voltage profile, thereby reducing the total system costs. Also, obtained numerical analyzes show 0.34%, 0.14% and 0.12% improvement, in comparison to genetic algorithms, particle community and artificial bee colony, respectively. [ABSTRACT FROM AUTHOR]

• Developed a tactical supply chain optimization model for forest biomass gasification • The model was applied to the case of syngas production at a Kraft pulp mill in BC • Energy demand was identified as the most important uncertain parameter • The total cost of the supply chain increased by 67% when uncertainty was considered • Robust optimization provided a feasible solution for all uncertainty intervals Production of bioenergy and biofuels from forest-based biomass provides an economically and environmentally attractive use of an otherwise wasted material. However, from the collection to conversion, supply chain of forest-based biomass is tainted with many challenges and uncertainties that impact its economic performance. In the literature, most papers focused on the long-term optimization of forest-based biomass gasification supply chains using deterministic models. In this paper, the supply chain of forest-based biomass for syngas production is optimized at the tactical level considering uncertainties. A robust optimization model is developed and applied to the real case of a large Kraft pulp mill in British Columbia, Canada. The total supply chain cost of the case study based on the robust optimization model is 67% higher than that of the deterministic model, however, the robust model provides a single optimal solution that is feasible for all parameter values within their considered uncertainty intervals. [ABSTRACT FROM AUTHOR]

Abstract: The separation of acetone from chloroform is difficult because the highly nonideal vapor–liquid equilibrium produces a maximum-boiling azeotrope. An earlier paper (Luyben, W. L. (2008). Control of the maximum-boiling acetone/chloroform azeotropic distillation system. Industrial & Engineering Chemistry Research, 47, 6140–6149) discussed the use of extractive distillation for making this separation. This paper studies the use of pressure-swing distillation for making the same separation. Results show that the extractive distillation process is much more attractive from the standpoint of both capital investment and energy consumption. But pressure-swing distillation avoids the potential problem of product contamination by the extractive solvent that must be added to the binary system. [Copyright &y& Elsevier]

Buildings are known for being great consumers of energy and for operating under On/Off and linear control strategies in common commercial packages. In order to reduce their energy footprints and to improve thermal comfort, new methodologies are needed to obtain better input profiles. This paper presents a detailed study of the implementation of a nonlinear model predictive control (NMPC) approach for a heating, ventilation, and air conditioning (HVAC) system. The HVAC system is modeled by an index-1 differential-algebraic system of equations, obtained from rigorous material and energy balances. The proposed NMPC algorithm was implemented in GAMS and compared with another approach from the literature. Similarly, the objective function is defined using one or more of the following criteria: (1) tracking of temperature and relative humidity set points; (2) maximization of thermal comfort; (3) minimization of energy consumption. To better represent the situation in a real application, the simulations include model-plant mismatch in parameters such as air infiltration into the building, the coefficient of the thermal exchange between the building and the exterior, and occupancy level. Additionally, simulations with random measurement noise have been performed. The results have shown that the proposed approach, in which the model is not linearized at any step, is able to reduce the energy consumption and maintain the Predicted Mean Vote (PMV) close to the desired set point, while the computation burden is only increased by one second per iteration, which is negligible in comparison with a sampling time of 10 min. [ABSTRACT FROM AUTHOR]

• Uncertainties in by-product gas scheduling are considered. • The proposed rolling-horizon algorithm maximizes energy efficiency. • BFG, COG and LDG gases are forecasted in real time using time series models. • Short-term decisions are calculated iteratively by solving a MILP problem. • Forecasting error effect in the algorithm performance is analyzed. This paper addresses the real-time by-product gas scheduling in an integrated iron- and steel-making industry with uncertainty in by-product gas flows from a rolling horizon algorithm. Adaptive time-series models determined from real data performs forecast for each producer and consumer of by-product gases in main units of the steel-making plant. The individual consumptions of the blast furnace and coke oven gases are modelled using the seasonal Holt-Winters method with smoothing constants estimated via genetic algorithm, whereas the individual productions of the blast furnace and coke oven are identified from autoregressive and integrated moving-average. LDG gas production is forecasted using a heuristic method that leverages the operational information. The model's parameters are updated periodically due to the nonlinearities present in the time series. After the forecasting phase, the algorithm performs short-term decisions using a MILP optimization model, that minimizes the imbalance between the random dynamics of by-product fuel generation and consumption and maximizes the energy efficiency. Through computational simulations, we show that the operational stability of gas holders and the electrical energy production increase, whereas the waste of gas in flare stack decreases, when the control horizon of the rolling horizon algorithm is reduced. [ABSTRACT FROM AUTHOR]

Highlights • Use of a novel framework Process Resilience Analysis Framework. • Method to determine optimal maintenance strategies based on trade-off analysis. • Definition of System Survivability Index as the system safety threshold. • Consideration of safety impact costs in the expected revenue objective function. • Resilience Survivability Index metric for system effectiveness in process upsets. • Incorporation of quantitative social metrics in the analysis. Abstract In a process plant system, safe and reliable operations are highly sensitive to utilities such as power, steam, cooling water, nitrogen, and instrument air. These play an important role or act as safety barriers. This is because a disturbance in their supply is likely to affect process operations downstream, may reduce the production efficiency, and may lead to a sudden shutdown or contribute to an unsafe condition. The focus of this paper is to introduce and evaluate a model for survival of a process system under upset conditions using the Process Resilience Analysis Framework (PRAF). Resilience metrics within a data-driven and model-based optimization approach using Bayesian regression are employed integrating both technical (process parameter variations) and social (human and organizational) factors. Based on an optimization objective function accounting for the overall system performance in terms of energy consumption, maintenance costs, safety impact, environmental impact, asset damage, and production loss, the proposed methodology aims to determine the optimal maintenance policy for optimal and safer plant operations. The implementation of the survival model within the PRAF is demonstrated on a cooling tower operation example problem, where optimal operation and maintenance (preventive, corrective, and predictive) strategies are determined based on trade-off analysis of process revenue, safety impact, and maintenance costs. [ABSTRACT FROM AUTHOR]

Abstract: Systems technologies emerge with a powerful potential to support the deployment and design of future biorefineries. The chemical industry experiences a steady growth in the use of renewables induced by the gradual depletion of oil, uncertainties in energy supplies and a commanding requirement to reduce GHG emissions and save the planet. Renewables introduce an impressive range of options with biorefining at the center of attention as an emerging industrial concept, uniquely attached to chemical engineering and aiming to transform plant-derived biomass into a variety of products including transport fuels, platform chemicals, polymers, and specialty chemicals. In competing with conventional processes, biorefineries should match maximum efficiencies with better design and process integration. The paper highlights the pivotal role of systems technology to foster innovation, preview options, and support high-throughput computational experimentation, arguing that systems tools are largely under-deployed. Systems-enabled platforms could instead function as powerful environments to generate ideas for integrated designs and offer tremendous services to the complex and large problems produced by the numerous portfolios of feedstocks, unknown portfolios of products, multiple chemistries, and multiple processing paths. Complexities certainly exceed capabilities of previous methodologies but established achievements and experience with similar problems are excellent starting points for future contributions. Besides a general discussion, the paper outlines opportunities for innovation in design, concept-level synthesis, process integration, and the development of supply chains. [Copyright &y& Elsevier]

• Distillation pressure selection drastically affects energy costs. • Vacuum operation frequently offers significant savings in energy costs. • The pressure-swing distillation of the THF-water azeotrope is studied to demonstrate pressure selection-effects. • Reduction in energy cost of 50% are achieved. Papers dealing with the design of distillation systems continue to be published in which operating pressures are frequently arbitrarily assumed. Since pressure can profoundly affect relative volatilities in both ideal and azeotropic separations, design pressure should be carefully considered as a critical design optimization variable. The purpose of this paper is to illustrate this important concept using a design proposed in a recently published paper of a pressure-swing distillation process. Alternative designs are generated that reduce energy consumption and total annual cost by a factor of two compared to the original design. [ABSTRACT FROM AUTHOR]

The synthesis of complex energy systems usually involves large time series such that a direct optimization is computationally prohibitive. In this paper, we propose a decomposition method for synthesis problems using time-series aggregation. To initialize the method, the time series is aggregated to one time step. A lower bound is obtained by relaxing the energy balances and underestimating the energy demands leading to a relaxed synthesis problem, which is efficiently solvable. An upper bound is obtained by restricting the original problem with the full time series to an operation problem with a fixed structure obtained from the lower bound solution. If the bounds do not satisfy the specified optimality gap, the resolution of the time-series aggregation is iteratively increased. The decomposition method is applied to two real-world synthesis problems. The results show the fast convergence of the decomposition method outperforming commercial state-of-the-art optimization software. [ABSTRACT FROM AUTHOR]

Continuous Pharmaceutical Manufacturing (CPM) is a promising new paradigm to produce active pharmaceutical ingredients (APIs), allowing reduced equipment dimensions, lower waste production and energy consumption, and safer operation in comparison to the industrially dominant batch methods. Rufinamide is an antiepileptic agent whose demonstrated continuous flow synthesis (featuring three reactions in flow) circumvents the accumulation of toxic and explosive organoazide intermediates. To ascertain the feasibility and viability of this continuous synthetic route, systematic process modelling and costing is required. This paper presents a technoeconomic analysis of the upstream continuous flow synthesis of rufinamide via steady-state process modelling and plantwide simulation. Reaction kinetics and Arrhenius parameters are estimated from previously published experimental data, and plug flow reactor (PFR) volumes are calculated towards rigorous plant costing. Continuous reactor and separator units have been designed, and the CPM flowsheet is compared vs. the batch production method, with respect to technical efficiency and profitability. Plantwide costing via an established economic analysis methodology has been pursued to enable a detailed comparison of cost items towards process scale-up, as well as motivate the need for further systematic optimisation. [ABSTRACT FROM AUTHOR]

Low and medium temperature energy utilization is one way to alleviate the energy crisis and environmental pollution problems. In the past decades, Organic Rankine Cycles (ORCs) have become a very promising technology for low and medium temperature energy utilization. When an ORC is used to recover waste heat in chemical plants, heat integration between the ORC and the process streams should be performed to save more utilities and generate more power. This study aims to integrate an ORC into a background process to generate maximum electricity without increasing the hot utility usage. We propose a two-step method to integrate an ORC to a background process, optimally considering the modifications of the ORC to increase the thermal efficiency and heat recovered by the working fluid simultaneously. The first step is to determine the configuration (turbine bleeding, regeneration, superheating) and operating conditions (working fluid flowrate, evaporation and condensation temperatures, turbine bleed ratio, degree of superheat, bleeding pressure). The second step is to synthesize the heat exchanger network by minimizing the number of heat exchangers that keep the hot utility unchanged. A well-studied example from the literature is solved to demonstrate the effectiveness of the proposed model for industrial waste heat recovery. The net power output in this paper is improved by 13% compared with the best known previous literature design for this system. The proposed method is also useful for quickly screening working fluids while considering integration potential. Screening of several working fluids revealed that using R601 (n-pentane) in place of the original working fluid (n-hexane) can increase the power output of the example system by an additional 14%. [ABSTRACT FROM AUTHOR]

The development and implementation of alternative energy conversion techniques using renewable energy sources is critical for a sustainable economy. Among renewable energy sources, solar energy is prominent due to its abundance. Towards a sustainable economy, this paper presents a process design concept to synthesize Solar Electricity, Water, Food and Chemical (SEWFAC) processes. The proposed approach entails systematic synthesis of energy efficient, synergistic processes incorporating process intensification for optimal utilization of resources. The objective is the development of coproduction processes around the clock on an as-needed basis. A general strategy and detailed analysis to synthesize efficient solar thermal hydrogen production processes through solar thermal power cogeneration. Process simulations and optimizations are performed using an integrated MATLAB and Aspen Plus modeling environment. The proposed process designs are evaluated based on the various metrics introduced. Process designs are estimated to achieve 11% higher exergy efficiency compared to the traditional processes. [ABSTRACT FROM AUTHOR]

Distillation is an energy intensive operation in chemical process industries. There are around 40,000 distillation columns in operation in the US, requiring approximately 40% of the total energy consumption in US chemical process industries. However, analysis of separations by distillation has shown that more than 50% of energy is spent in purifying the last 5–10% of the distillate product. Membrane modules on the other hand can achieve high purity separations at lower energy costs, but if the flux is high, it requires large membrane area. A hybrid scheme where distillation and membrane modules are combined such that each operates at its highest efficiency, has the potential for significant energy reduction without significant increase of capital costs. This paper presents a method for sustainable design of hybrid distillation-membrane schemes with guaranteed reduction of energy consumption together with two illustrative examples. [ABSTRACT FROM AUTHOR]

In recent years there has been increased concern on energy efficiency of industries. Since the scheduling problems in the shop floors are directly related to the energy consumption, an appropriate way to improve energy efficiency in industrial plants is to develop effective scheduling strategies. Hence, the aim of this paper is to design an energy-efficient scheduling in a shop floor industrial environment, i.e., flexible job-shop scheduling problem (FJSP). To this end, a multi-objective optimization model is developed with three objective functions: (i) minimizing total completion time, (ii) maximizing the total availability of the system, and (iii) minimizing total energy cost of both production and maintenance operations in the FJSP. To cope with this multi-objective optimization problem, an enhanced evolutionary algorithm incorporated with the global criterion, as a multi-objective handling technique, is proposed and then performance evaluation is performed based on an extensive numerical analysis. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for optimising the exergy efficiency of atmospheric distillation unit without trading off the products qualities and process throughput. The presented method incorporates the second law of thermodynamics in data driven models. Bootstrap aggregated neural networks (BANN) are used for enhanced model accuracy and reliability. The standard error of the individual neural network predictions is taken as the indication of model prediction reliability and is incorporated in the optimization objective function. The economic analysis of the recoverable energy (sum of internal and external exergy losses) reveals the energy saving potential of the proposed method, which will aid the design and operation of energy efficient atmospheric distillation columns. [ABSTRACT FROM AUTHOR]

This paper presents an economic Dynamic Real-Time Optimization (DRTO) of the operation of a solar thermal plant. The methodology includes a planning phase, used to improve storage management. A storage state target is included in the economic objective function of the DRTO, with a weight adjusted through a sensitivity analysis. The methodology is tested with a simulation model in a case study representing different seasons. The simulation results using the trajectories for the flow rates obtained with the DRTO algorithm or with the offline planning phase are compared, for several real-time scenarios. Thanks to our DRTO methodology, the operating cost and the excess energy delivered were reduced, the part of solar energy used to satisfy the heat demand was increased and a reasonable tracking of the storage state target was achieved. This study brings good perspectives for the implementation of our DRTO methodology on an actual plant. • An economic Dynamic Real-Time Optimization is applied to a solar thermal plant. • A planning phase is used to determine the storage management policy. • A case study representing several seasons and disturbances scenarios is presented. • The operating costs of the plant were reduced and the solar fraction was increased. • The objective on the tracking of the planned storage state is optimally included. [ABSTRACT FROM AUTHOR]

Natural gas is usually transported by pipeline networks. This paper presents a multi-objective optimization model in optimizing the operation of natural gas pipeline networks. A mathematical model is established for different network configurations, namely linear, branch and looped topologies. Two conflicting objectives, power consumption minimization and gas delivery flow rate maximization, are considered subjected to pipeline and compressor constraints. The decision variables are the pressure at the nodes and the rotational speed of the compressors. A set of constraints is developed based on gas flow equations and compressors operating conditions. Due to the nonlinearity of the constraints and the objective, the developed model is a Nonlinear Programming problem. The optimization of the models is performed with NSGA-II algorithm. The solution obtained is a set of Pareto optimal points from which a decision maker can select a specific preferred solution. Sensitivity analysis was performed to determine the impact of parameter changes. [ABSTRACT FROM AUTHOR]

Inter-city high-speed passenger transport, mainly aviation and high-speed railway, has been increasing around the world, in accordance with economic development and penetration of high-speed transport technologies. The energy consumption over the lifetime of transport infrastructure and operation is a significant factor at the planning stage. In this paper, we present a superstructure modelling and optimization framework of inter-city high-speed transport systems, accounting energy consumption during infrastructure construction and during subsequent operation, to optimize connections between large population centers and between modes of transport. Energy consumption during infrastructure construction is obtained from investment cost using lifecycle assessment. The first two cases considered differences between infrastructure construction and lifetime operation while the second case narrowed the study scope. Sensitivity analysis in the third case compared impacts of both transport means on system design. Model results have implications for actual high-speed transport technology development and infrastructure layout. [ABSTRACT FROM AUTHOR]

• A data-driven bi-level model integrating planning and scheduling for PVC production is proposed. • Scheduling of a mixed batch/continue process with the variable production rate under uncertainty is investigated. • The practical results show the effectiveness and advantages of energy-efficient scheduling measures. PVC production by the calcium carbide method is a typical hybrid system consisting of continuous and intermittent processing, and the optimization of the whole process scheduling is of practical significance. Still, the existence of uncertain factors in the external environment and production process makes it challenging to continue to implement deterministic scheduling. This paper, therefore, proposes an integration model, the discrete-time method applied in the planning layer, and the chance-constrained programming used to deal with external uncertainties. The continuous-time modeling method based on event points is adopted in the scheduling layer, and the optimization of equipment-level operations with variable production rates is performed. Then, the data-driven robust optimization method is applied to deal with endogenous uncertainty based on the data-driven flexible uncertainty set. Finally, the results provide valuable guidance to improve the robustness and stability of the production system under uncertainty. The comparative results verify the effectiveness and superiority of the proposed model in total cost and energy consumption. [ABSTRACT FROM AUTHOR]

• Optimization of RON fed by decarbonated seawater with permeate split is proposed. • Variation of pH values is fitted by experiment and simulated results from literature. • Water cost and energy consumption are reduced significantly by PSDS design. • PSDS design is a robust solution for pH control. • Robust and flexible designs considering uncertainties are obtained. This paper presents a superstructure based optimization of reverse osmosis (RO) system fed by decarbonated seawater (DS) at high pH value under boron restrictions. Each RO stage/pass could be operated with high boron rejection. The permeate split (PS) design allows permeates with different quality collecting from both sides of pressure vessel (PV). The pH variations are fitted by experiment and simulated results from the literature. The accuracy of the RO model is also validated. Results showed that about 3.48%−18.31% water cost and 3.04%−14.61% energy consumption savings are achieved by permeate split with decarbonated seawater (PSDS) design. Water cost is less influenced by pH values for PSDS design, which might be a robust solution for pH control. A two-stage stochastic model is performed to obtain robust and flexible designs considering uncertainties of the RO design problem. Both operational conditions and process flow are adjusted to save water cost and energy consumption. [Display omitted] [ABSTRACT FROM AUTHOR]

• Control approach suitable for implementation on embedded platforms. • Optimization-free evaluation of close-to-optimal control inputs. • Guarantee of asymptotic stability with respect to physical constraints. • The benefits of the approach demonstrated using a 32-bit microcontroller. • Reduction of computational time and energy consumption. Advanced, optimization-based control methods are implemented at each level of industrial production. Although the model predictive control (MPC) represents a state-of-the-art control strategy maximizing profit, and, simultaneously, minimizing the energy losses, its industrial implementation is limited by the requirements on the software and hardware resources. This paper proposes an approximated MPC for such cases when the implementation of implicit MPC has a prohibitive effort on industrial hardware and its explicit counterpart is limited by its memory footprint requirements. The recursive random shooting-based approach is introduced to eliminate the conventional optimization and minimize the memory requirements, meanwhile guarantying the asymptotic stability subject to the physical constraints on control inputs and system states. The benefits of the proposed method are significantly reduced computational complexity by 93%, and decreased energy consumption by 93%, compared to non-recursive random shooting, while the performance loss is approximately 8%. [ABSTRACT FROM AUTHOR]

Finding good process schemes is a difficult task due to its search among multiple alternatives. In literature, several methods are successfully implemented as computer tools, but their use is limited to their developers. There are several useful rules of thumb and heuristics providing useful guidelines, but sometimes these are contradictory. Some authors tried to rank the various alternatives, defining heuristic equations to provide a quantitative parameter to choose among possible solutions. In this paper, mathematical model of distillation columns is simplified, assuming infinite number of stages. The resulting proposed equation has a great similarity to one of the previous heuristic ones. A simple equation not relying on heuristics and easy to use in calculation is provided to evaluate the distillation sequence energy efficiency (DSE) for each alternative. This allows to quantify the advantages derived from process intensification for a given feed composition. The proposed equation is dimensionless, as the Carnot efficiency is used instead of the temperature difference between distillate and bottoms. On the other hand, intermediate results provide also useful information. For instance, the Carnot efficiency of each column indicates when a heat pump or enhanced distillation would be useful. The recovery efficiency for each particular compound allows an easy comparison between alternatives, considering variations on the feed composition. The new equation is verified comparing its results with cases already solved in literature using different methods. The results show that all the methods in literature are able to provide the best sequence, except the heuristics-based ones that are not providing an overall sequence evaluation. The novelty of the proposed DSE method resides in its ease of application, compared to nowadays available methods, and requires only the feed composition and products boiling points. [ABSTRACT FROM AUTHOR]

Vacuum/pressure swing adsorption is an attractive and often energy efficient separation process for some applications. However, there is often a trade-off between the different objectives: purity, recovery and power consumption. Identifying those trade-offs is possible through use of multi-objective optimisation methods but this is computationally challenging due to the size of the search space and the need for high fidelity simulations due to the inherently dynamic nature of the process. This paper presents the use of surrogate modelling to address the computational requirements of high fidelity simulations needed to evaluate alternative designs. We present SbNSGA-II ALM, surrogate based NSGA-II, a robust and fast multi-objective optimisation method based on kriging surrogate models and NSGA-II with the Active Learning MacKay (ALM) design criterion. The method is evaluated by application to an industrially relevant case study: a two column six step system for CO 2 /N 2 separation. A 5 times reduction in computational effort is observed. [ABSTRACT FROM AUTHOR]

In this paper, a novel enhanced-efficiency selection of operating variables based on self-optimizing control (SOC) method for the vapor compression refrigeration cycle (VCC) system is proposed. An objective function is proposed to maximize the energy efficiency of the VCC system while meeting with the demand of indoor thermal comfort. With the detailed analysis of operating variables, three unconstrained degrees of freedom are selected among all the candidate operating variables. Then two SOC methods are applied to determine the optimal individual controlled variables (CVs) and measurement combinations as CVs. The model predictive control (MPC) method based controllers and PID controllers are designed for different sets of CVs, and the experimental results indicate that the proposed selection of CVs can achieve a good trade-off between optimal (or near optimal) stable operation and enhanced-efficiency of the synthesized control structure. [ABSTRACT FROM AUTHOR]

On industrial sites, the promotion of best practices to enable an efficient utilization of energy has emerged as one of the major point of focus. Among the different approaches existing to improve industrial processes, the exergy analysis, although limited to the academic world, has been shown to be a powerful tool for improving energy efficiency of thermal and chemical systems. The purpose of this paper is then to present the use of the ProSimPlus ® modelling and simulation environment as an exergy analysis computer-aided tool. Expressions implemented in the simulator for computing exergies in its various forms are presented. The adopted approach for calculating exergy efficiency in a systematic way is also exposed; it combines the fuel-product concepts to the transit exergy concept. ProsimPlus ® exergy module's capabilities are illustrated through the example of an ammonia production plant. [ABSTRACT FROM AUTHOR]

Abstract: Industrial symbiosis (IS) is a subdiscipline of Industrial Ecology that aims to bring together companies from different sectors to share resources, namely energy, materials, and water. The main goal of IS is to improve resource (materials, waste, energy) efficiency and lead to mutual environmental, financial and social benefits to participants. In this paper we present a semantic approach for IS input/output matching. This approach is based on knowledge modelling and ontologies. Ontologies are used to model all resources – waste, water, energy – along with details about their composition, characteristics (chemical and physical) and tacit knowledge about their flow. The input/output matching algorithm presented enables the valorisation of resources through industrial symbiosis networks. [Copyright &y& Elsevier]

Abstract: Concerns about diminishing petroleum reserves, enhanced worldwide demand for fuels and fluctuations in the global oil market, together with climate change and national security have promoted many initiatives for exploring alternative, non-petroleum based processes. Among these initiatives, biorefinery processes for converting biomass derived carbohydrates into transportation fuels and chemicals are now gaining more and more attention from both academia and industry. Process synthesis, which has played a vital role for the development, design and operation of (petro) chemical processes, can be predicted to play a significant role in the design and commercialization of sustainable and cost-effective biorefinery processes. The main objective of this perspective paper is to elucidate the potential opportunities that biorenewables processing offers to optimal synthesis; challenges and future directions in this field are also concisely discussed. An attempt is made with this perspective to stimulate more and more efforts to optimally synthesize and design biorenewable conversion process to accelerate the commercialization of the biorefinery technology and further reduce the heavily reliance on petroleum-derive fuels. [Copyright &y& Elsevier]

Abstract: Economic, energy, and sustainability metrics are key performance indicators for process operations. The relative importance of these metrics varies from plant to plant, and often some metrics are in conflict with each other (sustainability vs. profitability). In this paper we discuss the current plant environment and how various metrics can be aligned by focusing on energy efficiency. Power-steam systems are the major energy drivers for most plants, and we discuss possible operational changes that might improve energy efficiency, as well as the role of process control. Managing the interplay of real-time optimization and regulatory control is a challenge for the future, as well as interfacing with the implementation of smart power grids by the utility industry. Combined heat and power along with energy storage presents interesting control and optimization opportunities to maximize energy efficiency. [Copyright &y& Elsevier]

Abstract: The importance of balancing social, environmental and economic objectives in companies’ development has cultivated a growing awareness on the sustainable optimal design and planning of supply chains. In the past years, significant research effort has been devoted to extend current approaches to capture these objectives in order to guarantee long term sustainability. Among the various approaches developed, inventory management, product design, production planning and control for remanufacturing, product recovery, reverse logistics and closed-loop supply chains have gained more attention in the literature. In this paper, we review some of the relevant research on sustainable chemical processes and supply chain design focusing on three main areas: (i) sustainable supply chains with respect to energy efficiency and waste management, (ii) environmentally sustainable supply chains and (iii) sustainable water management. The emerging challenges in this area are summarized, and future opportunities are highlighted. [Copyright &y& Elsevier]

Abstract: In a naphtha distillation process, the natural objective is to perform an entire process maximization of the production rate while meeting required product qualities by searching for an optimal operating condition by manipulating the operating variables. The objective of this paper includes performing an energy process optimization. Not only is an adequate production rate met with the required product qualities but the operating cost is also minimized through a data mining approach. The study of the influence of all process attributes in the defined Energy Efficient Indicator (EEI) allows the construction of a multivariate linear model to aid human experts in the recovery of energy losses. A canonical discriminant function carried out the data prediction step. The quality of the Decision Support System framework is illustrated by a case study considering a real database. Also, a commercial software supported by this mining framework is presented. [Copyright &y& Elsevier]

Abstract: LNG liquefaction is an energy-intensive process. Energy efficiency is the major concern in LNG liquefaction process design and operation. In this paper, a new methodology for LNG liquefaction synthesis targeting energy consumption minimization is presented. It is based on thermodynamic analysis, mathematical programming, and rigorous simulation. The procedure for MINLP (mixed-integer non-linear programming) model development and simplification is described in detail. LINDOGlobal solver in GAMS is employed to solve the MINLP problem and the optimization results are further validated through rigorous simulations. Based on the development, the optimal design and operational conditions for LNG liquefaction process can be simultaneously achieved. A C3MR LNG liquefaction process is employed to demonstrate the efficacy of the developed methodology. Comprehensive thermodynamic analysis is also conducted to give insights of the optimization results. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to minimise the energy requirement of autothermal thermophilic aerobic digestion (ATAD). To this end, a dynamic ATAD model is presented and assessed. A global sensitivity analysis was performed to identify the operating conditions with the strongest impact on the energy requirements, and thus to choose the most promising optimisation variables. The latter turned out to be the aeration flowrate, the reaction time, and the sludge flowrate. The optimisation problem was formulated following the sequential approach for dynamic optimisation, due to the discontinuous nature of ATAD. The problem was implemented in MATLAB® and solved for two case studies using the eSS algorithm, a global scatter search method that alternates with local algorithms (in our case fmincon) to refine the best solutions. The two selected full-scale case studies include a single-stage and a two-stage system. For the former, a 22% improvement of the energy requirement was achieved after optimisation, and 18% for the latter. Despite its advantages and common use in other fields, optimisation is still relatively rare in wastewater engineering. In the light of the high, rising cost of wastewater treatment, optimisation should become the norm when it comes to design and operation of wastewater treatment plants. [Copyright &y& Elsevier]

Abstract: This work presents a new plant-wide control strategy able to be applied on large scale chemical plants. It is based on an extension of the non square relative gain array (NRG) theoretical concepts, introduced by Chang and Yu (1990), and the generalized relative disturbance gain (GRDG) presented in Chang and Yu (1992). The extension of the NRG is useful for searching the best group of controlled variables (CVs) independently of the problem dimensionality. Meanwhile, the extension of the GRDG allows configure the loops pairing by considering the trade-off between servo and regulator behavior. It can be done thanks to define a proper function, named net load effect, accounting both set point and disturbances effects. Even though these concepts are not new, the main contribution of this paper is the selection of the adequate objective function. It is mathematically expressed in a new way, in terms of Frobenius norm of specific matrices related with the models of the plant and very useful for evaluating the process interaction. Then, it drives the search supported by genetic algorithms (GA), which evaluates all the possible combinations of input–output variables. It allows to solve successfully and with less computational effort the combinatorial optimization problem, even though the high dimension usually involved in large scale chemical plants. The use of the relative gain array (RGA) can also be considered for pairing purpose, but in some cases it could drive to a less effective structure. The use of relative normalized gain array (RNGA) for pairing the selected CVs with the most suitable MVs is able to lead to best control structures only if a dynamic model of the plant is available. Therefore, it must be emphasized that this approach is developed for working in cases where only steady-state plant information is available. However, if a dynamic model is disposable too the algorithm is extended to use it. In addition, a mathematical demonstration is presented so as to understand why is possible to find a well conditioned control structure. The methodology is tested in the Tennessee Eastman (TE) process at the base case proposed by Downs and Vogel (1992), and at an optimized working point presented by Ricker (1995). Both working points show two quite different scenarios. Thus, a set of dynamic simulations for both cases and the hardware requirements compared to the previous suggested are given to proof the capacity of this approach. [Copyright &y& Elsevier]

Abstract: Polygeneration energy systems have drawn great attention because of their superiority over conventional stand-alone plants in energy efficiency and emissions control. Although many approaches have been made to the process design of polygeneration energy systems, most of them use coal gasification as the only gas source and produce only a single type of chemical product. In this paper, we present a dual-gas sourced process design which utilizes, besides synthetic gas (syngas) from coal gasification, coke oven gas as gas sources and co-produces methanol, dimethyl ether, and dimethyl carbonate via an integrated catalytic synthesis procedure. A process simulation based on detailed chemical kinetics is presented to illustrate its feasibility. Results of an exergoeconomic analysis are provided to indicate the exergy loss in each functional block within the process. Based on the analysis, an exergy cost distribution diagram is presented, showing the production cost of a product over each functional block. [Copyright &y& Elsevier]

Abstract: Though commonly encountered in practice, energy and water minimization simultaneously during batch process scheduling has been largely neglected in literature. In this paper, we present a novel framework for incorporating simultaneous energy and water minimization in batch process scheduling. The overall problem is decomposed into three parts – scheduling, heat integration, and water reuse optimization – and solved sequentially. Our approach is based on the precept that in any production plant, utilities (energy and water) consumption is subordinate to the production target. Hence, batch scheduling is solved first to meet an economic objective function. Next, alternate schedules are generated through a stochastic search-based integer cut procedure. For each resulting schedule, minimum energy and water reuse targets are established and networks identified. As illustrated using two well-known case studies, a key feature of this approach is its ability to handle problems that are too complex to be solved using simultaneous methods. [Copyright &y& Elsevier]

Abstract: This paper presents a scheduling solution for electrical load tracking of a steel plant. In the scheduling problem the electricity consumption of the plant is pre-specified by an energy curve. During production all tasks must be scheduled such that the total electricity consumption of all machines tracks the scheduled consumption as closely as possible while respecting all production constraints. All energy over- and underconsumption is penalized since it results in fines from the electricity producer. The problem is formulated as a mixed integer programming problem. The scheduling model uses a continuous time formulation. The model is tested on production data from industry. The results show that the continuous time scheduling model can be successfully applied for the electrical load tracking scheduling of a steel plant. By using this type of scheduling the plant owner can potentially minimize over- and underconsumption and thereby reduce his costs. [Copyright &y& Elsevier]

Abstract: Separation processes account for more than half of capital and operating costs in chemical manufacturing. Separations are the energy intensive operations in the chemical industry. Rising energy consumption combined with the environmental impact increases the need for energy saving separation processes. Fortunately complex column networks have the potential for major energy savings estimated to range between 30 and 70% over simple column configuration. In this paper, a computer-aided synthesis method is introduced to synthesize optimal complex column arrangements which encode the cost and states of global solutions with minimum user input. A robust feasibility criterion helps select design and operating conditions for the entire network that can be realized in practice. Our method builds on thermodynamic transformations entitled temperature collocation which provides crucial advantages to determine the operating conditions, structure, and size of the separation network for achieving the desired product cuts. The computational approach guarantees realizable column profiles, which can be validated with industrially accepted simulation software such as AspenHysys and AspenPlus. [Copyright &y& Elsevier]

Abstract: There has recently been a renewed interest in the design of distillation processes due to rising energy cost and growing environmental concerns. Column profile maps (CPMs) have been developed as a graphical tool to simplify the design procedure of distillation schemes as well as a method for analyzing existing processes. Using CPMs one is able to change topology within the composition space to suit the requirements of the separation and hence many separations that have been thought of as difficult or unviable can now be better understood and consequently new designs may be devised. The CPM technique has also been proven to be extremely useful as a design tool for complex columns, as configurations irrespective of complexity can be modeled and graphically understood. This paper aims to summarize the most important and interesting results and applications obtained using the CPM technique. It shows how CPMs may be used to synthesize complex columns like a Petlyuk or Kaibel column, as well as showing how new sharp split separations can be devised. [Copyright &y& Elsevier]

Abstract: Cryogenic air separation, while widely used in industry, is an energy intensive process. Effective design can improve efficiency and reduce energy consumption, however, uncertainties can make determination of the optimal design difficult. This paper addresses the conceptual design of cryogenic air separation process under uncertainty. A rigorous, highly nonlinear model of three integrated columns is developed to capture the coupled nature of the process. The multi-scenario approach is used to incorporate the uncertainty, giving rise to a nonlinear programming problem with over half a million variables. Nevertheless, this problem is solved efficiently using Ipopt, demonstrating the effectiveness of interior-point methods on complex, large-scale nonlinear programming problems. The optimal design from the multi-scenario approach is compared against the optimal design using nominal parameter values. As expected, the results using the multi-scenario approach are more conservative than the nominal case; however, they may be less conservative than traditional overdesign factors. [Copyright &y& Elsevier]

Abstract: Several authors have pointed out the need to identify the optimum operating conditions (OCs) of autothermal thermophilic aerobic digestion (ATAD). This study proves the hypothesis that the OCs have the potential to substantially improve the energy efficiency and plant capacity of established ATAD systems. As ATAD is a semi-batch process, its energy efficiency has to be optimized via dynamic optimization (DO). This methodology requires an adequate mathematical model, and appropriate selection of optimization variables. The paper presents an improved mathematical ATAD model based on previous models found in the literature. A global sensitivity analysis (GSA) was performed in order to identify variables with significant influence upon energy efficiency and plant capacity, thus paving the way for the DO of ATAD systems. The results of the GSA show that reactor volume, reactor temperature, and aeration flowrate are significant variables, which is consistent with reported literature. The results of the GSA also show that both energy efficiency and plant capacity of ATAD systems can be substantially improved by altering reactor volume and OCs. [Copyright &y& Elsevier]

Abstract: The integrated gasification combined cycle (IGCC) is an electrical power generation system which offers efficient generation from coal with lower effect on the environment than conventional coal power plants. However, further improvement of its efficiency and thereby lowering emissions are important tasks to achieve a more sustainable energy production. In this paper, a process simulation tool is proposed for simulation of IGCC. This tool is used to improve IGCC''s efficiency and the environmental performance through an analysis of the operating conditions, together with process integration studies. Pinch analysis principles and process integration insights are then employed to make topological changes to the flowsheet to improve the energy efficiency and minimize the operation costs. Process data of the Texaco gasifier and the associated plants (coal preparation, air separation unit, gas cleaning, sulfur recovery, gas turbine, steam turbine and the heat recovery steam generator) are considered as a base case, and simulated using Aspen Plus®. The results of parameter analysis and heat integration studies indicate that thermal efficiency of 45% can be reached, while a significant decrease in CO2 and SO x emissions is observed. The CO2 and SO x emission levels reached are 698kg/MWh and 0.15kg/MWh, respectively. Application of pinch analysis determines energy targets, and also identifies potential modifications for further improvement to overall energy efficiency. Benefits of energy integration and steam production possibilities can further be quantified. Overall benefits can be translated to minimum operation costs and atmospheric emissions. [Copyright &y& Elsevier]

Abstract: For a successful large scale implementation of biomass-to-liquid fuel for transportation, it is imperative that production of liquid fuel from biomass be maximized. For this purpose, synergistic processes using energy from sustainable carbon-free energy sources are needed. In this paper, we present such novel integrated processes that, when compared to the known conventional conversion methods, have potential to produce nearly three times more liquid fuel from a given quantity of biomass. The new processes treat biomass predominantly as carbon source and rely on the novel integrations to preserve carbon atoms during biomass conversion to liquid fuel. We have named such approach as hybrid hydrogen–carbon (H2CAR) process. Furthermore, we propose a novel synergistic integration between H2CAR and fermentation process where high-level heat from the H2CAR process is used to supply process heat for the fermentation process and CO2 produced during the fermentation is converted to liquid fuel using H2CAR process. This synergy leads to increase in process carbon efficiency (∼100%) and higher energy efficiency (65.7% vs. 57.2%), significantly decreasing land area requirement to produce liquid fuel compared to fermentation-based processes. Such synergistically integrated processes provide attractive opportunities for process design, operation and control. [Copyright &y& Elsevier]

Abstract: The design and optimization of a coupled multicomponent distillation system is a non-linear and multivariable problem. The complexity of this kind of problem results in high solving difficulty. This paper addresses the application of genetic algorithms to the optimization of intensified distillation systems for quaternary distillations. We used a multiobjective genetic algorithm with restrictions coupled to the Aspen Plus™ process simulator for the evaluation of the objective function. Several mixtures to test the effect of relative volatilities of feed mixtures on energy consumption, second law efficiency, total annual cost and theoretical control properties have been studied. Numerical performance shows that this design tool is robust and suitable for the design of coupled multicomponent distillation sequences. [Copyright &y& Elsevier]

Abstract: This paper presents a kind of hierarchical inference strategy integrating quality prediction model for the optimal-setting control of blending process in alumina metallurgical industry. By integrating mechanistic model with intelligent compensator based on neural networks and feedback of indices, the prediction model is built to predict raw slurry quality. The target of raw slurry quality is online adjusted according the historical production of blending process and the current requirements of downstream process. Based on the biases between the prediction results and the target values of quality, a hierarchical inference strategy is proposed to determine an optimal set-point of raw material proportioning (RMP) in real time. The practical running results show that the eligibility rate of raw slurry is effectively improved, and the blending process is successfully simplified as well as the energy consumption is obviously reduced. [Copyright &y& Elsevier]

Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to maintain the effluent quality within regulations-specified limits. Good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. In presence of influent disturbances, two approaches have been studied: the addition of a feedforward action based on the measurement of the influent flow rate; the use of nonlinear model predictive controller by addition of a penalty function. The effects of two approaches were visible on the decrease of ammonium and nitrogen concentration which were considered as being of major importance. The results show that MPC can be effectively used for control in wastewater treatment process. By comparing performances, the nonlinear model predictive control strategy with penalty function demonstrates best with small effluent quality index and acceptable aeration and pumping energy consumption. [Copyright &y& Elsevier]

• Decentralised data based control using PLC-informed Reinforcement Learning. • Distillation of available control code into neural network based RL. • Energy Optimization of modular production systems allowing for plug-and-produce operation. • Application to different production scenarios including continuous and batch operations. • Considerable reduction of training times due to using simple PLC-code. This paper presents a novel approach to distributed optimization in production systems using reinforcement learning (RL) with particular emphasis on energy efficient production. Unlike existing approaches in RL which learn optimal policies from scratch, we speed-up the learning process by considering available control code of the Programmable Logic Controller (PLC) as a baseline for further optimizations. For such PLC-informed RL, we propose Teacher-Student RL to distill the available control code of the individual modules into a neural network which is subsequently optimized using standard RL. The proposed general framework allows to incorporate PLC control code with different level of detail. We implement the approach on a laboratory scale testbed representing different production scenarios ranging from continuous to batch production. We compare the results for different control strategies which show that comparably simple control logic yields considerable improvements of the optimization compared to learning from scratch. The obtained results underline the applicability and potential of the approach in terms of improved production efficiency while considerably reducing the energy consumption of the production schedules. [ABSTRACT FROM AUTHOR]